Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting
In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinea...
Saved in:
| Published in | Journal of agricultural, biological, and environmental statistics Vol. 20; no. 1; pp. 100 - 120 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer Science+Business Media, LLC
01.03.2015
Springer US Springer |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. Supplementary materials accompanying this paper appear on-line. |
|---|---|
| AbstractList | In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. Supplementary materials accompanying this paper appear on-line. Electronic Supplementary Material Supplementary materials for this article are available at 10.1007/s13253-014-0180-3. In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies “bad actors” in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. Supplementary materials accompanying this paper appear on-line. In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. Supplementary materials accompanying this paper appear on-line. In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set.In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. |
| Audience | Academic |
| Author | Factor-Litvak, Pam Gennings, Chris Wheeler, David C. Carrico, Caroline |
| Author_xml | – sequence: 1 givenname: Caroline surname: Carrico fullname: Carrico, Caroline – sequence: 2 givenname: Chris surname: Gennings fullname: Gennings, Chris – sequence: 3 givenname: David C. surname: Wheeler fullname: Wheeler, David C. – sequence: 4 givenname: Pam surname: Factor-Litvak fullname: Factor-Litvak, Pam |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30505142$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkk1v1DAQhiNURD_gB3AA-QiHFI8dO9kL0mr5aKVKiF0QR8tJJlkvWbu1HcTy6_GStmI5FFmWLfl5Zzwz72l2ZJ3FLHsO9BwoLd8E4EzwnEKRdkVz_ig7AcHLnMkZP0p3Wom8BCiPs9MQNpQCl5Q9yY45FVRAwU6yzWKtvW4ievNLR-MscR35hqZfR2zJ51HbaAYkq3FLlth7DGHPdM6Ti8QMO7Jw3uOg9_Q7HTUxlmiyNOE7mVs97IIJZIUxGts_zR53egj47PY8y75-eP9lcZFfffp4uZhf5U1JZcxlJ3XNZ03LKAOuq7LhKFtRN6wqULd1LTSXuhJclK1sKdcgUJaia1ktsKsoP8veTnGvx3qLbYM2ej2oa2-22u-U00YdvlizVr37oSSTIKhMAV7dBvDuZsQQ1daEBodBW3RjUAyKGQUQqff_Q6EEyhJfQkLPJ7TXAypjO5eSN2m1uDVNGmyXGq3mRRopAIe94PWBIDERf8ZejyGoy9XykH35d833xd4NOgHlBDTeheCxU42JfwaefmEGBVTtLaUmS6lkKbW3lOJJCf8o74I_pGGTJiTW9ujVxo0--SE8KHoxiTYhOn-fhclCsBkv-G_5K-aR |
| CitedBy_id | crossref_primary_10_1016_j_ecoenv_2023_115546 crossref_primary_10_1016_j_envres_2024_119165 crossref_primary_10_1016_j_jclepro_2024_143156 crossref_primary_10_1289_EHP10125 crossref_primary_10_1016_j_intimp_2021_108428 crossref_primary_10_18332_pht_144201 crossref_primary_10_3390_atmos15101157 crossref_primary_10_1016_j_enceco_2025_03_001 crossref_primary_10_1007_s11356_023_29318_7 crossref_primary_10_1007_s12561_023_09405_6 crossref_primary_10_1016_j_ecoenv_2023_114687 crossref_primary_10_1016_j_chemosphere_2021_129664 crossref_primary_10_1007_s11356_022_22352_x crossref_primary_10_1016_j_envres_2021_110764 crossref_primary_10_1289_EHP4918 crossref_primary_10_1111_liv_15492 crossref_primary_10_1007_s11356_023_30177_5 crossref_primary_10_1289_EHP13864 crossref_primary_10_1016_j_scitotenv_2023_165151 crossref_primary_10_1093_hropen_hoae051 crossref_primary_10_3389_fnut_2022_962705 crossref_primary_10_1186_s12940_024_01096_w crossref_primary_10_1038_s41370_019_0162_1 crossref_primary_10_1007_s12403_022_00471_7 crossref_primary_10_1136_bmjopen_2019_032758 crossref_primary_10_1016_j_scitotenv_2024_176311 crossref_primary_10_1016_j_scitotenv_2022_160203 crossref_primary_10_1007_s00420_024_02047_1 crossref_primary_10_3389_fpubh_2025_1572360 crossref_primary_10_1186_s12889_024_20926_7 crossref_primary_10_1186_s12940_019_0526_y crossref_primary_10_1007_s11356_023_31423_6 crossref_primary_10_1080_10253890_2025_2477530 crossref_primary_10_3390_ijerph192416746 crossref_primary_10_1186_s12940_025_01156_9 crossref_primary_10_1016_j_envint_2019_105185 crossref_primary_10_1016_j_envres_2023_116838 crossref_primary_10_1038_s41370_018_0081_6 crossref_primary_10_1016_j_ecoenv_2023_115312 crossref_primary_10_1016_j_envres_2019_108922 crossref_primary_10_1289_EHP10570 crossref_primary_10_1007_s11356_021_17794_8 crossref_primary_10_1016_j_jes_2024_01_043 crossref_primary_10_1038_s41598_024_76972_z crossref_primary_10_1002_fsn3_4613 crossref_primary_10_1016_j_envres_2020_110009 crossref_primary_10_1016_j_chemosphere_2022_135134 crossref_primary_10_3390_ijerph191912421 crossref_primary_10_1016_j_scitotenv_2023_169565 crossref_primary_10_1016_j_envres_2024_120598 crossref_primary_10_1016_j_envres_2022_114115 crossref_primary_10_1016_j_envres_2023_115720 crossref_primary_10_1007_s11356_023_26119_w crossref_primary_10_1214_20_AOAS1364 crossref_primary_10_3945_jn_117_253419 crossref_primary_10_1186_s12967_024_06021_w crossref_primary_10_1016_j_ijheh_2023_114238 crossref_primary_10_1016_j_scitotenv_2023_167385 crossref_primary_10_3390_ijerph16193700 crossref_primary_10_1016_j_ecoenv_2022_114130 crossref_primary_10_1038_s41598_024_82673_4 crossref_primary_10_1186_s12889_024_18209_2 crossref_primary_10_1016_j_ecoenv_2023_115733 crossref_primary_10_1016_j_envpol_2023_122389 crossref_primary_10_1186_s12889_024_20349_4 crossref_primary_10_3389_fnut_2023_1205537 crossref_primary_10_1007_s11356_023_27181_0 crossref_primary_10_1038_nrendo_2016_186 crossref_primary_10_3390_metabo13020158 crossref_primary_10_1016_j_jtemb_2024_127477 crossref_primary_10_1016_j_envpol_2022_118864 crossref_primary_10_1016_j_envres_2015_09_013 crossref_primary_10_1016_j_scitotenv_2022_160883 crossref_primary_10_1021_acs_est_4c02080 crossref_primary_10_1016_j_envres_2022_113019 crossref_primary_10_3390_ijerph18126576 crossref_primary_10_1016_j_chemosphere_2023_137870 crossref_primary_10_1186_s12940_019_0515_1 crossref_primary_10_3389_fncom_2023_1302010 crossref_primary_10_3390_toxics12070503 crossref_primary_10_1016_j_envint_2024_108872 crossref_primary_10_1016_j_envres_2024_119148 crossref_primary_10_1016_j_scitotenv_2024_173085 crossref_primary_10_1016_j_envres_2024_118293 crossref_primary_10_1016_j_chemosphere_2024_142052 crossref_primary_10_1016_j_ijheh_2023_114210 crossref_primary_10_3389_fnut_2024_1486741 crossref_primary_10_3390_ijerph19020694 crossref_primary_10_1016_j_scitotenv_2024_173083 crossref_primary_10_1186_s12940_023_01009_3 crossref_primary_10_1002_sim_9765 crossref_primary_10_1016_j_envpol_2022_119926 crossref_primary_10_1016_j_envres_2023_115943 crossref_primary_10_1016_j_envres_2022_114322 crossref_primary_10_1007_s12011_024_04458_0 crossref_primary_10_1016_j_hrtlng_2025_03_006 crossref_primary_10_1016_j_envres_2021_111435 crossref_primary_10_1111_jcpe_13919 crossref_primary_10_1007_s40572_017_0162_z crossref_primary_10_1016_j_ecoenv_2023_115384 crossref_primary_10_1016_j_envres_2024_118912 crossref_primary_10_1007_s11356_023_29932_5 crossref_primary_10_1007_s11356_022_24958_7 crossref_primary_10_1097_EDE_0000000000001492 crossref_primary_10_1007_s11356_022_21062_8 crossref_primary_10_1016_j_ijheh_2019_113446 crossref_primary_10_1016_j_ijheh_2023_114270 crossref_primary_10_2139_ssrn_3952432 crossref_primary_10_3390_ijerph19042273 crossref_primary_10_37871_jbres1298 crossref_primary_10_3390_ijerph19031369 crossref_primary_10_1016_j_envpol_2022_120727 crossref_primary_10_1016_j_ecoenv_2024_116525 crossref_primary_10_1016_j_ecoenv_2024_116765 crossref_primary_10_1016_j_envpol_2024_124669 crossref_primary_10_1289_EHP11606 crossref_primary_10_1016_j_scitotenv_2020_141735 crossref_primary_10_3390_nu10081093 crossref_primary_10_1016_j_envres_2025_121131 crossref_primary_10_3390_toxics13010009 crossref_primary_10_1080_09603123_2024_2431246 crossref_primary_10_3390_ijerph19031378 crossref_primary_10_1016_j_ecoenv_2023_115371 crossref_primary_10_1016_j_envres_2022_115067 crossref_primary_10_1007_s11356_022_21391_8 crossref_primary_10_1016_j_envpol_2024_125527 crossref_primary_10_1097_EDE_0000000000001272 crossref_primary_10_1016_j_scitotenv_2022_154362 crossref_primary_10_1016_j_envint_2022_107300 crossref_primary_10_1016_j_envint_2023_107816 crossref_primary_10_1016_j_envint_2025_109362 crossref_primary_10_1038_s41398_020_01041_8 crossref_primary_10_1038_s41598_025_88765_z crossref_primary_10_1186_s12940_024_01065_3 crossref_primary_10_1016_j_envint_2021_107075 crossref_primary_10_1007_s11524_024_00829_z crossref_primary_10_1073_pnas_2208886119 crossref_primary_10_1016_j_envres_2023_115350 crossref_primary_10_1016_j_scitotenv_2022_158788 crossref_primary_10_1016_j_ecoenv_2024_116950 crossref_primary_10_1016_j_envpol_2024_124694 crossref_primary_10_1016_j_envint_2019_104993 crossref_primary_10_1016_j_envres_2023_117986 crossref_primary_10_1016_j_ecoenv_2023_115346 crossref_primary_10_1016_j_envint_2021_107004 crossref_primary_10_1007_s10653_024_01929_0 crossref_primary_10_1080_02664763_2022_2061430 crossref_primary_10_3389_fpubh_2022_832079 crossref_primary_10_1016_j_envpol_2019_113548 crossref_primary_10_1021_acs_est_4c00074 crossref_primary_10_1016_j_jhazmat_2024_134224 crossref_primary_10_1007_s10653_024_02048_6 crossref_primary_10_1007_s11356_021_18317_1 crossref_primary_10_1016_j_envpol_2022_119780 crossref_primary_10_1016_j_envres_2023_117507 crossref_primary_10_1016_j_scitotenv_2023_165365 crossref_primary_10_3390_ijerph20010094 crossref_primary_10_1007_s11356_024_32745_9 crossref_primary_10_1016_j_envres_2022_114187 crossref_primary_10_1016_j_envres_2023_115334 crossref_primary_10_1016_j_scitotenv_2023_168621 crossref_primary_10_1016_j_scitotenv_2024_172185 crossref_primary_10_1111_biom_13702 crossref_primary_10_3389_fnut_2024_1465875 crossref_primary_10_1016_j_envint_2022_107597 crossref_primary_10_1289_EHP10318 crossref_primary_10_1021_acs_est_4c01156 crossref_primary_10_1186_s12889_024_20702_7 crossref_primary_10_3390_s20175002 crossref_primary_10_1016_j_envres_2019_01_054 crossref_primary_10_1186_s40001_024_01785_9 crossref_primary_10_3390_toxics13010046 crossref_primary_10_1016_j_chemosphere_2020_128467 crossref_primary_10_1007_s12561_023_09410_9 crossref_primary_10_1021_acs_est_3c04688 crossref_primary_10_1093_jncics_pkae122 crossref_primary_10_1289_EHP5838 crossref_primary_10_1016_j_envres_2020_109212 crossref_primary_10_1016_j_ijheh_2024_114335 crossref_primary_10_1021_acs_est_4c00094 crossref_primary_10_1016_j_ijheh_2024_114336 crossref_primary_10_1016_j_scitotenv_2023_165586 crossref_primary_10_1016_j_chemosphere_2022_134471 crossref_primary_10_1016_j_ecoenv_2024_116981 crossref_primary_10_3389_fpubh_2024_1401420 crossref_primary_10_1016_j_envres_2023_116880 crossref_primary_10_1016_j_envint_2021_107039 crossref_primary_10_1016_j_envres_2020_109466 crossref_primary_10_1016_j_chemosphere_2022_136428 crossref_primary_10_1016_j_neuro_2021_11_008 crossref_primary_10_1007_s40572_023_00402_x crossref_primary_10_1016_j_envpol_2024_124495 crossref_primary_10_1016_j_mex_2019_11_008 crossref_primary_10_1016_j_chemosphere_2023_140334 crossref_primary_10_1021_envhealth_4c00097 crossref_primary_10_1016_j_cofs_2024_101151 crossref_primary_10_3390_ijerph182412881 crossref_primary_10_1016_j_envint_2018_02_044 crossref_primary_10_3390_nu16121901 crossref_primary_10_1016_j_chemosphere_2023_138494 crossref_primary_10_1016_j_ecoenv_2024_117440 crossref_primary_10_1016_j_reprotox_2023_108495 crossref_primary_10_1016_j_envint_2024_108486 crossref_primary_10_1016_j_ecoenv_2025_117952 crossref_primary_10_1016_j_jad_2024_08_089 crossref_primary_10_3390_children8080673 crossref_primary_10_1007_s12403_021_00439_z crossref_primary_10_1016_j_genhosppsych_2023_12_005 crossref_primary_10_1016_j_chemosphere_2021_133235 crossref_primary_10_3390_toxics12050316 crossref_primary_10_1016_j_envpol_2022_119356 crossref_primary_10_1136_bmjopen_2021_058286 crossref_primary_10_1016_j_neuro_2025_02_005 crossref_primary_10_1016_j_scitotenv_2022_156869 crossref_primary_10_1289_EHP12097 crossref_primary_10_1016_j_envres_2024_119989 crossref_primary_10_1016_j_envres_2021_112276 crossref_primary_10_1126_science_abe8244 crossref_primary_10_3389_fonc_2023_1239147 crossref_primary_10_1007_s12403_023_00573_w crossref_primary_10_1097_EE9_0000000000000225 crossref_primary_10_1016_j_ecoenv_2025_117949 crossref_primary_10_1016_j_envres_2023_116394 crossref_primary_10_1016_j_ijheh_2024_114493 crossref_primary_10_1016_j_envres_2022_113607 crossref_primary_10_1016_j_envres_2022_113609 crossref_primary_10_1016_j_jad_2024_07_021 crossref_primary_10_3390_nu14193915 crossref_primary_10_1016_j_scitotenv_2022_153548 crossref_primary_10_1016_j_envres_2022_113606 crossref_primary_10_3390_metabo11080545 crossref_primary_10_1007_s10067_024_07156_3 crossref_primary_10_1016_j_envpol_2024_124043 crossref_primary_10_1289_EHP8739 crossref_primary_10_1126_sciadv_aat1293 crossref_primary_10_1007_s11356_022_23148_9 crossref_primary_10_1038_s41380_022_01669_6 crossref_primary_10_1177_11786302231225313 crossref_primary_10_1016_j_envpol_2021_117097 crossref_primary_10_1016_j_jhazmat_2024_134312 crossref_primary_10_1016_j_ntt_2024_107355 crossref_primary_10_1126_science_adp1870 crossref_primary_10_1016_j_ecoenv_2024_116142 crossref_primary_10_1016_j_ecoenv_2024_117473 crossref_primary_10_1016_j_scitotenv_2024_172638 crossref_primary_10_1016_j_ecoenv_2024_116310 crossref_primary_10_1007_s13762_023_05275_z crossref_primary_10_1038_s41467_024_49283_0 crossref_primary_10_1159_000541875 crossref_primary_10_1016_j_jtemb_2023_127243 crossref_primary_10_1007_s10653_024_01949_w crossref_primary_10_1016_j_envint_2023_107920 crossref_primary_10_1080_09603123_2025_2451626 crossref_primary_10_1016_j_chemosphere_2022_135741 crossref_primary_10_1016_j_chemosphere_2022_134416 crossref_primary_10_3389_fcell_2024_1390247 crossref_primary_10_1007_s11356_024_33266_1 crossref_primary_10_1007_s12403_022_00489_x crossref_primary_10_1016_j_chemosphere_2023_139144 crossref_primary_10_1016_j_jacc_2024_07_023 crossref_primary_10_1016_j_jhazmat_2024_134507 crossref_primary_10_1016_j_msard_2022_104243 crossref_primary_10_1016_j_envres_2023_117234 crossref_primary_10_1016_j_envpol_2024_125152 crossref_primary_10_1016_j_envint_2019_105212 crossref_primary_10_1016_j_envpol_2020_114537 crossref_primary_10_1016_j_envpol_2023_121730 crossref_primary_10_1016_j_envres_2020_109840 crossref_primary_10_1021_acs_est_3c06013 crossref_primary_10_1016_j_envpol_2023_121965 crossref_primary_10_1038_s41598_023_41865_0 crossref_primary_10_1016_j_etap_2023_104233 crossref_primary_10_1007_s11356_023_28218_0 crossref_primary_10_3390_toxics10110692 crossref_primary_10_1007_s11356_022_21208_8 crossref_primary_10_1016_j_envint_2019_105424 crossref_primary_10_1016_j_envint_2024_109167 crossref_primary_10_1016_j_earlhumdev_2021_105450 crossref_primary_10_1016_j_envint_2021_106697 crossref_primary_10_3389_fnut_2025_1524099 crossref_primary_10_1007_s12011_021_02972_z crossref_primary_10_1016_j_scitotenv_2023_165622 crossref_primary_10_3389_fnut_2022_921550 crossref_primary_10_3390_ijerph18031254 crossref_primary_10_1161_HYPERTENSIONAHA_121_18809 crossref_primary_10_3389_frph_2023_1285444 crossref_primary_10_1016_j_teac_2022_e00161 crossref_primary_10_1016_j_ecoenv_2024_116572 crossref_primary_10_1186_s12940_022_00855_x crossref_primary_10_1016_j_envpol_2020_114557 crossref_primary_10_1016_j_envres_2023_117459 crossref_primary_10_1210_clinem_dgad425 crossref_primary_10_1016_j_envint_2022_107615 crossref_primary_10_1016_j_jpsychires_2022_06_006 crossref_primary_10_1016_j_envres_2023_116361 crossref_primary_10_1038_s43856_024_00683_9 crossref_primary_10_1007_s11356_023_29938_z crossref_primary_10_1016_j_ecoenv_2020_111854 crossref_primary_10_3389_fmicb_2023_1193919 crossref_primary_10_1021_acs_est_3c09503 crossref_primary_10_1186_s40246_022_00428_6 crossref_primary_10_3389_fpubh_2024_1367644 crossref_primary_10_3389_fpubh_2024_1377685 crossref_primary_10_1016_j_chemosphere_2021_133015 crossref_primary_10_1016_j_rasd_2020_101681 crossref_primary_10_3390_ijerph21020222 crossref_primary_10_1016_j_envint_2019_105003 crossref_primary_10_4103_aja202237 crossref_primary_10_1016_j_scitotenv_2023_169127 crossref_primary_10_1016_j_scitotenv_2024_178116 crossref_primary_10_1186_s12940_023_00985_w crossref_primary_10_1016_j_jtemb_2024_127566 crossref_primary_10_1186_s12937_025_01079_8 crossref_primary_10_1007_s11270_025_07869_4 crossref_primary_10_1016_j_envres_2022_113225 crossref_primary_10_1007_s40572_023_00417_4 crossref_primary_10_1016_j_chemosphere_2023_138650 crossref_primary_10_1016_j_isci_2024_111335 crossref_primary_10_1039_D3FO02920D crossref_primary_10_3390_toxics10030116 crossref_primary_10_2139_ssrn_4176357 crossref_primary_10_1053_j_gastro_2024_10_041 crossref_primary_10_1016_j_ecoenv_2025_117798 crossref_primary_10_3389_fendo_2021_774142 crossref_primary_10_2139_ssrn_4182819 crossref_primary_10_1016_j_jad_2024_08_004 crossref_primary_10_1186_s12889_024_19414_9 crossref_primary_10_1097_EE9_0000000000000092 crossref_primary_10_1016_j_chemosphere_2023_139973 crossref_primary_10_1371_journal_pone_0249236 crossref_primary_10_1016_j_envres_2022_114305 crossref_primary_10_1016_j_scitotenv_2024_170639 crossref_primary_10_1002_cad_20363 crossref_primary_10_1016_j_chemosphere_2023_138644 crossref_primary_10_1289_ehp_1408630 crossref_primary_10_1016_j_envres_2024_119109 crossref_primary_10_1016_j_envint_2024_108433 crossref_primary_10_1186_s12916_024_03596_5 crossref_primary_10_1016_j_envint_2020_106374 crossref_primary_10_1016_j_envint_2023_108117 crossref_primary_10_1016_j_envint_2021_106409 crossref_primary_10_1002_jpn3_12346 crossref_primary_10_1371_journal_pone_0316045 crossref_primary_10_3390_toxics12090642 crossref_primary_10_1016_j_envres_2024_120325 crossref_primary_10_1016_j_ypmed_2024_108217 crossref_primary_10_1186_s12937_023_00898_x crossref_primary_10_1289_EHP14418 crossref_primary_10_1016_j_chemosphere_2019_124412 crossref_primary_10_1016_j_envint_2024_108425 crossref_primary_10_1016_j_scitotenv_2023_162935 crossref_primary_10_1016_j_ecoenv_2022_114209 crossref_primary_10_1186_s12889_024_20897_9 crossref_primary_10_1017_S0007114523003070 crossref_primary_10_3920_WMJ2022_2784 crossref_primary_10_1016_j_annepidem_2016_11_016 crossref_primary_10_3390_ijerph18020504 crossref_primary_10_1016_j_envres_2024_120514 crossref_primary_10_1016_j_jtemb_2024_127524 crossref_primary_10_1016_j_jhazmat_2020_124378 crossref_primary_10_1016_j_envres_2024_118434 crossref_primary_10_1016_j_envint_2023_108176 crossref_primary_10_1016_j_jhazmat_2025_137214 crossref_primary_10_1016_j_envint_2020_105461 crossref_primary_10_1016_j_chemosphere_2024_143084 crossref_primary_10_1016_j_envint_2020_106312 crossref_primary_10_1002_JPER_23_0079 crossref_primary_10_3389_fpubh_2024_1476953 crossref_primary_10_1186_s12940_020_00608_8 crossref_primary_10_1016_j_envres_2022_114759 crossref_primary_10_1016_j_scitotenv_2024_173954 crossref_primary_10_1016_j_pmedr_2023_102235 crossref_primary_10_1016_j_ecoenv_2023_114921 crossref_primary_10_1016_j_scs_2025_106293 crossref_primary_10_3389_fnut_2023_1129169 crossref_primary_10_1016_j_envint_2024_108447 crossref_primary_10_1007_s10661_024_13534_w crossref_primary_10_1007_s12403_023_00591_8 crossref_primary_10_2139_ssrn_4111404 crossref_primary_10_1016_j_diabet_2024_101602 crossref_primary_10_1016_j_envint_2023_108183 crossref_primary_10_1016_j_scitotenv_2024_172614 crossref_primary_10_1289_EHP9875 crossref_primary_10_1016_j_scitotenv_2024_171742 crossref_primary_10_1016_j_envint_2021_106612 crossref_primary_10_1016_j_envres_2019_108719 crossref_primary_10_1097_EE9_0000000000000262 crossref_primary_10_1016_j_jad_2025_03_049 crossref_primary_10_1016_j_chemosphere_2022_137065 crossref_primary_10_1021_acs_est_9b00392 crossref_primary_10_1371_journal_pone_0227219 crossref_primary_10_1038_s41598_019_42377_6 crossref_primary_10_1016_j_envres_2024_119776 crossref_primary_10_1186_s12986_023_00746_z crossref_primary_10_1016_j_envres_2024_118443 crossref_primary_10_1183_13993003_02149_2021 crossref_primary_10_1016_j_scitotenv_2023_161410 crossref_primary_10_1016_j_envpol_2023_121348 crossref_primary_10_3390_ijerph18073486 crossref_primary_10_1029_2023GH000796 crossref_primary_10_1007_s12011_024_04236_y crossref_primary_10_3390_e23121633 crossref_primary_10_1097_EDE_0000000000001777 crossref_primary_10_1186_s12302_023_00809_1 crossref_primary_10_1186_s12890_024_03128_0 crossref_primary_10_1016_j_jes_2024_08_020 crossref_primary_10_1016_j_heliyon_2024_e37837 crossref_primary_10_1016_j_envres_2020_110111 crossref_primary_10_1016_j_scitotenv_2021_152639 crossref_primary_10_1021_acs_est_3c08064 crossref_primary_10_2337_dc22_1585 crossref_primary_10_1186_s12940_020_00637_3 crossref_primary_10_1016_j_envres_2024_119277 crossref_primary_10_1016_j_envpol_2024_124943 crossref_primary_10_1038_s41598_024_75010_2 crossref_primary_10_1186_s12940_018_0400_3 crossref_primary_10_1016_j_envres_2020_110341 crossref_primary_10_1007_s13253_018_0326_9 crossref_primary_10_1016_j_scitotenv_2025_178934 crossref_primary_10_1016_j_heliyon_2024_e37840 crossref_primary_10_1016_j_envres_2023_115419 crossref_primary_10_1016_j_envres_2023_116508 crossref_primary_10_1016_j_envpol_2024_123866 crossref_primary_10_1016_j_ecoenv_2023_115208 crossref_primary_10_1016_j_envint_2024_108957 crossref_primary_10_1186_s13690_024_01275_8 crossref_primary_10_3389_fpubh_2024_1410601 crossref_primary_10_1016_j_ecoenv_2022_114279 crossref_primary_10_2139_ssrn_4142177 crossref_primary_10_1007_s11356_023_27578_x crossref_primary_10_3390_ijerph18073614 crossref_primary_10_1016_j_envres_2021_111747 crossref_primary_10_1016_j_envint_2022_107494 crossref_primary_10_1016_j_jhazmat_2021_126963 crossref_primary_10_1016_j_ijheh_2021_113909 crossref_primary_10_1007_s12561_023_09404_7 crossref_primary_10_1016_j_envres_2023_116965 crossref_primary_10_2139_ssrn_4123445 crossref_primary_10_1016_j_envres_2022_113157 crossref_primary_10_1007_s12011_023_03901_y crossref_primary_10_1177_20406207231189922 crossref_primary_10_1016_j_envres_2025_121439 crossref_primary_10_1007_s11356_023_27717_4 crossref_primary_10_1016_j_envres_2020_109148 crossref_primary_10_1016_j_scitotenv_2023_166347 crossref_primary_10_1016_j_puhe_2022_06_015 crossref_primary_10_1097_PXR_0000000000000322 crossref_primary_10_1007_s12403_024_00629_5 crossref_primary_10_1016_j_jad_2021_12_133 crossref_primary_10_1016_j_envint_2022_107083 crossref_primary_10_1038_s41370_024_00739_x crossref_primary_10_1186_s12889_024_19971_z crossref_primary_10_1016_j_envpol_2025_126113 crossref_primary_10_1016_j_envres_2018_10_029 crossref_primary_10_1007_s11356_023_28739_8 crossref_primary_10_1016_j_envres_2021_112615 crossref_primary_10_1289_EHP10285 crossref_primary_10_1016_j_envpol_2021_118452 crossref_primary_10_1371_journal_pone_0317678 crossref_primary_10_3390_toxics12080613 crossref_primary_10_1016_j_envres_2021_111764 crossref_primary_10_1016_j_envres_2020_109396 crossref_primary_10_1016_j_rasd_2018_08_003 crossref_primary_10_1016_j_envint_2022_107078 crossref_primary_10_1007_s11356_023_28704_5 crossref_primary_10_1186_s12940_024_01103_0 crossref_primary_10_1016_j_envpol_2024_124987 crossref_primary_10_1016_j_envres_2024_119232 crossref_primary_10_1016_j_envint_2020_105970 crossref_primary_10_1097_MOP_0000000000000877 crossref_primary_10_1016_j_jhazmat_2025_137980 crossref_primary_10_1016_j_envpol_2025_126103 crossref_primary_10_1016_j_envpol_2022_119833 crossref_primary_10_3390_nu14081552 crossref_primary_10_1016_j_envint_2020_105728 crossref_primary_10_1016_j_ecoenv_2025_118044 crossref_primary_10_1038_s41467_024_46595_z crossref_primary_10_1016_j_envres_2021_111539 crossref_primary_10_1016_j_ecoenv_2023_115881 crossref_primary_10_1021_acs_est_4c13095 crossref_primary_10_1289_EHP9478 crossref_primary_10_1016_j_neuro_2023_01_008 crossref_primary_10_1016_j_scitotenv_2024_174055 crossref_primary_10_1089_lgbt_2021_0088 crossref_primary_10_1097_EDE_0000000000001351 crossref_primary_10_35371_aoem_2023_35_e23 crossref_primary_10_1097_EDE_0000000000001356 crossref_primary_10_1007_s12012_025_09969_3 crossref_primary_10_1016_j_envres_2024_118175 crossref_primary_10_1038_s41467_025_57253_3 crossref_primary_10_1002_sim_9888 crossref_primary_10_1097_JOM_0000000000003002 crossref_primary_10_3389_fpubh_2022_980987 crossref_primary_10_1016_j_ecoenv_2020_111284 crossref_primary_10_1016_j_scitotenv_2022_158218 crossref_primary_10_1186_s12889_024_20546_1 crossref_primary_10_1007_s11356_022_21090_4 crossref_primary_10_1016_j_envint_2022_107297 crossref_primary_10_1016_j_jtemb_2024_127573 crossref_primary_10_1016_j_envpol_2024_123679 crossref_primary_10_1016_j_envint_2020_105753 crossref_primary_10_1038_s41370_024_00726_2 crossref_primary_10_1038_s41598_021_89846_5 crossref_primary_10_1007_s11356_022_19916_2 crossref_primary_10_1016_j_envpol_2022_118969 crossref_primary_10_1016_j_envres_2024_120483 crossref_primary_10_1093_biostatistics_kxac001 crossref_primary_10_1016_j_envint_2018_07_028 crossref_primary_10_1016_j_sste_2019_100286 crossref_primary_10_1016_j_envpol_2023_123163 crossref_primary_10_1016_j_ecoenv_2024_116634 crossref_primary_10_1289_EHP1374 crossref_primary_10_1016_j_ijheh_2024_114430 crossref_primary_10_1186_s12940_020_00644_4 crossref_primary_10_1186_s13098_024_01556_w crossref_primary_10_1007_s11356_023_26828_2 crossref_primary_10_1016_j_envint_2020_105941 crossref_primary_10_1016_j_envres_2020_109994 crossref_primary_10_1371_journal_pone_0313675 crossref_primary_10_1016_j_envres_2022_112932 crossref_primary_10_1007_s11356_023_26926_1 crossref_primary_10_1093_exposome_osae001 crossref_primary_10_1186_s12940_023_01023_5 crossref_primary_10_1186_s12889_025_22010_0 crossref_primary_10_1007_s12403_024_00653_5 crossref_primary_10_1016_j_ecoenv_2023_115253 crossref_primary_10_1016_j_ijheh_2018_03_010 crossref_primary_10_1016_j_chemosphere_2024_143610 crossref_primary_10_1016_j_chemosphere_2023_139054 crossref_primary_10_1016_j_ecoenv_2023_115256 crossref_primary_10_1007_s11356_023_28706_3 crossref_primary_10_1186_s12940_024_01088_w crossref_primary_10_3389_fpubh_2025_1555731 crossref_primary_10_1016_j_envres_2020_109529 crossref_primary_10_1016_j_jhazmat_2024_135220 crossref_primary_10_1016_j_jhazmat_2020_124905 crossref_primary_10_1021_acs_est_4c04500 crossref_primary_10_1186_s12889_024_21204_2 crossref_primary_10_1186_s12916_022_02403_3 crossref_primary_10_1007_s11356_023_28902_1 crossref_primary_10_1021_acs_est_4c11066 crossref_primary_10_1016_j_envpol_2025_125686 crossref_primary_10_1016_j_lanwpc_2024_101100 crossref_primary_10_1007_s11356_023_30317_x crossref_primary_10_1016_j_envint_2022_107422 crossref_primary_10_3389_fendo_2024_1329247 crossref_primary_10_1016_j_ijheh_2023_114136 crossref_primary_10_3389_fnut_2021_754663 crossref_primary_10_1016_j_envres_2020_110534 crossref_primary_10_1016_j_envres_2025_121266 crossref_primary_10_1214_24_AOAS1988 crossref_primary_10_1007_s11356_022_18871_2 crossref_primary_10_1016_j_puhe_2023_12_021 crossref_primary_10_1093_biostatistics_kxac038 crossref_primary_10_1016_j_eehl_2022_09_001 crossref_primary_10_1016_j_envpol_2024_125415 crossref_primary_10_1371_journal_pone_0301097 crossref_primary_10_1016_j_jes_2024_09_008 crossref_primary_10_1016_j_ijheh_2024_114422 crossref_primary_10_3390_ijerph19127399 crossref_primary_10_1016_j_ecoenv_2025_118062 crossref_primary_10_1016_j_chemosphere_2024_143882 crossref_primary_10_1016_j_scitotenv_2018_03_283 crossref_primary_10_3390_nu15132874 crossref_primary_10_1016_j_envint_2022_107249 crossref_primary_10_1016_j_scitotenv_2021_145237 crossref_primary_10_1093_aje_kwae115 crossref_primary_10_1016_j_envint_2022_107246 crossref_primary_10_1093_ije_dyaa259 crossref_primary_10_3389_fpubh_2023_1190948 crossref_primary_10_1097_PXR_0000000000000113 crossref_primary_10_1016_j_chemosphere_2024_142321 crossref_primary_10_1016_j_envint_2023_107771 crossref_primary_10_1111_pai_13732 crossref_primary_10_1016_j_chemosphere_2023_140085 crossref_primary_10_1016_j_envres_2023_117631 crossref_primary_10_1016_j_ecoenv_2023_115453 crossref_primary_10_1002_jbt_23745 crossref_primary_10_1186_s12940_024_01086_y crossref_primary_10_1186_s12940_021_00765_4 crossref_primary_10_1016_j_ijheh_2024_114483 crossref_primary_10_1186_s12940_022_00907_2 crossref_primary_10_1007_s11356_025_36089_w crossref_primary_10_3390_toxics12060438 crossref_primary_10_1016_j_jhepr_2023_100912 crossref_primary_10_3389_fpubh_2024_1411123 crossref_primary_10_1007_s11356_022_19836_1 crossref_primary_10_1186_s12937_024_01062_9 crossref_primary_10_3390_toxics12060430 crossref_primary_10_3390_sym14101962 crossref_primary_10_1016_j_scitotenv_2022_159935 crossref_primary_10_1214_21_AOAS1533 crossref_primary_10_1186_s12940_024_01073_3 crossref_primary_10_1289_EHP9294 crossref_primary_10_1016_j_envint_2018_12_030 crossref_primary_10_1186_s12889_025_21552_7 crossref_primary_10_1016_j_envpol_2025_125647 crossref_primary_10_1093_aje_kwab065 crossref_primary_10_1016_j_reprotox_2024_108544 crossref_primary_10_1289_EHP2207 crossref_primary_10_1265_jjh_22009 crossref_primary_10_1016_j_scitotenv_2023_164133 crossref_primary_10_3390_ijerph17238805 crossref_primary_10_1136_oemed_2023_109112 crossref_primary_10_3724_SP_J_1123_2023_09009 crossref_primary_10_1016_j_ecoenv_2024_116861 crossref_primary_10_1093_aje_kwad015 crossref_primary_10_3389_fendo_2024_1375374 crossref_primary_10_1214_21_AOAS1560 crossref_primary_10_1016_j_ecoenv_2023_115239 crossref_primary_10_1016_j_ecoenv_2024_116626 crossref_primary_10_1016_j_scitotenv_2024_175791 crossref_primary_10_1038_s41370_017_0008_7 crossref_primary_10_1016_j_ijheh_2020_113660 crossref_primary_10_1021_acs_est_3c11095 crossref_primary_10_1080_09603123_2024_2352609 crossref_primary_10_1016_j_envres_2021_111713 crossref_primary_10_1016_j_envint_2020_105900 crossref_primary_10_1080_03610918_2019_1577971 crossref_primary_10_1007_s40572_024_00467_2 crossref_primary_10_51387_24_NEJSDS58 crossref_primary_10_1016_j_envpol_2022_119894 crossref_primary_10_1038_s41366_022_01127_x crossref_primary_10_1016_j_chemosphere_2023_140690 crossref_primary_10_1016_j_envint_2018_09_035 crossref_primary_10_3390_toxics12110828 crossref_primary_10_1016_j_envres_2024_118511 crossref_primary_10_1038_s41467_024_45776_0 crossref_primary_10_1016_j_envint_2019_04_051 crossref_primary_10_1016_j_ntt_2023_107181 crossref_primary_10_3389_fpubh_2023_1241971 crossref_primary_10_1038_s41467_024_55080_6 crossref_primary_10_1016_j_envres_2021_112377 crossref_primary_10_1016_j_jes_2024_04_002 crossref_primary_10_1007_s40471_022_00296_7 crossref_primary_10_3390_ijms241512188 crossref_primary_10_1002_bimj_70033 crossref_primary_10_1016_j_ecoenv_2024_116472 crossref_primary_10_3389_fpubh_2025_1387702 crossref_primary_10_1016_j_cgh_2023_12_020 crossref_primary_10_3390_ijerph18041373 crossref_primary_10_1097_EE9_0000000000000135 crossref_primary_10_1016_j_chemosphere_2023_140200 crossref_primary_10_1016_j_envint_2022_107713 crossref_primary_10_1016_j_envint_2019_03_018 crossref_primary_10_1080_07853890_2023_2227844 crossref_primary_10_1080_10643389_2021_1970455 crossref_primary_10_2139_ssrn_4073661 crossref_primary_10_3390_toxics12120866 crossref_primary_10_1038_s41598_024_67728_w crossref_primary_10_1016_j_scitotenv_2024_170576 crossref_primary_10_1016_j_jes_2024_05_026 crossref_primary_10_1016_j_envint_2020_106098 crossref_primary_10_1289_EHP6202 crossref_primary_10_1186_s12891_023_06202_6 crossref_primary_10_1016_j_cotox_2021_03_005 crossref_primary_10_1093_aje_kwx183 crossref_primary_10_1007_s12011_022_03484_0 crossref_primary_10_1007_s11356_024_33563_9 crossref_primary_10_1016_j_ijheh_2024_114377 crossref_primary_10_1016_j_envres_2022_113962 crossref_primary_10_3389_fpubh_2023_1151821 crossref_primary_10_1038_s41370_024_00687_6 crossref_primary_10_1016_j_reprotox_2023_108393 crossref_primary_10_3389_fpubh_2022_995649 crossref_primary_10_1016_j_envres_2023_116049 crossref_primary_10_1097_EE9_0000000000000115 crossref_primary_10_1016_j_ijheh_2024_114380 crossref_primary_10_2903_sp_efsa_2022_EN_7555 crossref_primary_10_1007_s10995_023_03844_9 crossref_primary_10_1016_j_chemosphere_2021_131150 crossref_primary_10_1007_s11356_023_28065_z crossref_primary_10_1007_s12403_023_00594_5 crossref_primary_10_1007_s10654_020_00624_5 crossref_primary_10_1016_j_ecoenv_2024_117353 crossref_primary_10_1080_02664763_2024_2394784 crossref_primary_10_1038_s41598_024_58607_5 crossref_primary_10_1016_j_envpol_2021_116705 crossref_primary_10_1016_j_ecoenv_2024_116438 crossref_primary_10_1186_s12940_020_00642_6 crossref_primary_10_1016_j_scitotenv_2023_165518 crossref_primary_10_3389_fpubh_2024_1401034 crossref_primary_10_1002_cad_20458 crossref_primary_10_1016_j_jhazmat_2024_134864 crossref_primary_10_1016_j_envint_2018_08_039 crossref_primary_10_1038_s41370_018_0069_2 crossref_primary_10_1016_j_scitotenv_2024_174748 crossref_primary_10_1038_s41598_022_21747_7 crossref_primary_10_1111_risa_13323 crossref_primary_10_1186_s12940_024_01066_2 crossref_primary_10_1177_0033354919849882 crossref_primary_10_3389_fpubh_2023_1182127 crossref_primary_10_1016_j_scitotenv_2021_145759 crossref_primary_10_2217_epi_2016_0095 crossref_primary_10_1016_j_scitotenv_2023_163577 crossref_primary_10_1016_j_jhazmat_2024_134614 crossref_primary_10_1016_j_envint_2018_08_045 crossref_primary_10_1057_s41292_020_00192_7 crossref_primary_10_3390_brainsci13060902 crossref_primary_10_3390_nu15122677 crossref_primary_10_1016_j_ijheh_2022_113954 crossref_primary_10_1016_j_envres_2025_121171 crossref_primary_10_3390_nu16193282 crossref_primary_10_1016_j_chemosphere_2021_132065 crossref_primary_10_1097_EE9_0000000000000308 crossref_primary_10_1016_j_chemosphere_2020_128505 crossref_primary_10_1016_j_eehl_2024_02_005 crossref_primary_10_2139_ssrn_4118343 crossref_primary_10_1093_humrep_deae229 crossref_primary_10_1007_s11356_022_23093_7 crossref_primary_10_1016_j_envint_2018_08_010 crossref_primary_10_1038_s41598_024_67210_7 crossref_primary_10_3390_ijerph18031138 crossref_primary_10_1016_j_ijheh_2021_113689 crossref_primary_10_1016_j_chemosphere_2021_133147 crossref_primary_10_1016_j_envres_2019_108681 crossref_primary_10_1007_s10651_024_00610_0 crossref_primary_10_1007_s11356_022_22662_0 crossref_primary_10_1097_EE9_0000000000000316 crossref_primary_10_1186_s12889_024_20267_5 crossref_primary_10_1016_j_medp_2024_100058 crossref_primary_10_1089_heq_2020_0032 crossref_primary_10_2139_ssrn_3987971 crossref_primary_10_1016_j_scitotenv_2023_167909 crossref_primary_10_1016_j_envint_2021_106589 crossref_primary_10_1038_s41370_023_00524_2 crossref_primary_10_1016_j_envint_2018_11_076 crossref_primary_10_1016_j_chemosphere_2022_136940 crossref_primary_10_1210_clinem_dgae873 crossref_primary_10_1016_j_jhazmat_2024_133785 crossref_primary_10_3724_SP_J_1123_2023_12001 crossref_primary_10_1186_s12940_023_01012_8 crossref_primary_10_1007_s00204_022_03260_y crossref_primary_10_1016_j_placenta_2022_02_020 crossref_primary_10_1177_09622802211013578 crossref_primary_10_1016_j_envres_2024_119685 crossref_primary_10_1016_j_ajcnut_2024_11_026 crossref_primary_10_1183_20734735_0034_2023 crossref_primary_10_1080_10807039_2024_2440729 crossref_primary_10_1007_s12403_024_00638_4 crossref_primary_10_1111_ina_12508 crossref_primary_10_1016_j_envres_2022_114435 crossref_primary_10_1080_02664763_2020_1843611 crossref_primary_10_1016_j_chemosphere_2022_136077 crossref_primary_10_1016_j_jtemb_2024_127438 crossref_primary_10_1016_j_scitotenv_2021_151167 crossref_primary_10_1038_s41370_021_00370_0 crossref_primary_10_1007_s12561_023_09409_2 crossref_primary_10_1016_j_ajo_2024_05_019 crossref_primary_10_3389_fpubh_2024_1374959 crossref_primary_10_1016_j_scitotenv_2023_161745 crossref_primary_10_1016_j_scs_2024_106071 crossref_primary_10_1016_j_envres_2024_118580 crossref_primary_10_1016_j_envint_2020_106218 crossref_primary_10_1016_j_envpol_2023_122979 crossref_primary_10_3390_biomedicines10061438 crossref_primary_10_1007_s12011_024_04359_2 crossref_primary_10_1016_j_envpol_2023_121647 crossref_primary_10_1016_j_envres_2023_116902 crossref_primary_10_1016_j_scitotenv_2024_170754 crossref_primary_10_1002_acn3_51006 crossref_primary_10_1016_j_jtemb_2024_127428 crossref_primary_10_1016_j_jtemb_2024_127426 crossref_primary_10_1016_j_scitotenv_2023_169222 crossref_primary_10_1021_acs_est_6b03783 crossref_primary_10_1016_j_envint_2021_106770 crossref_primary_10_1016_j_envres_2019_108639 crossref_primary_10_3390_antiox11101991 crossref_primary_10_1016_j_envres_2025_120976 crossref_primary_10_1021_acs_est_3c00848 crossref_primary_10_3390_ijms23042320 crossref_primary_10_1016_j_ecoenv_2023_115726 crossref_primary_10_1016_j_chemosphere_2023_140626 crossref_primary_10_1016_j_ecoenv_2024_116091 crossref_primary_10_1080_10643389_2022_2093595 crossref_primary_10_1016_j_lanwpc_2023_100776 crossref_primary_10_1038_s41386_021_01066_7 crossref_primary_10_1016_j_ecoenv_2024_116097 crossref_primary_10_3389_fnut_2021_799095 crossref_primary_10_1016_j_envint_2019_105118 crossref_primary_10_1007_s13253_023_00528_3 crossref_primary_10_1016_j_envint_2023_108009 crossref_primary_10_1186_s12944_024_02153_6 crossref_primary_10_1289_EHP12118 crossref_primary_10_1111_biom_13569 crossref_primary_10_2139_ssrn_4102800 crossref_primary_10_1016_j_jhazmat_2024_133500 crossref_primary_10_1038_s41370_023_00568_4 crossref_primary_10_1097_EE9_0000000000000194 crossref_primary_10_1007_s11356_021_16936_2 crossref_primary_10_1289_EHP12596 crossref_primary_10_1016_j_envres_2021_112565 crossref_primary_10_1007_s10653_023_01791_6 crossref_primary_10_1186_s12874_024_02434_9 crossref_primary_10_1016_j_scitotenv_2024_170975 crossref_primary_10_1089_chi_2024_0215 crossref_primary_10_1016_j_envres_2024_119644 crossref_primary_10_1016_j_jad_2024_02_026 crossref_primary_10_1016_j_scitotenv_2022_161202 crossref_primary_10_1016_j_ecoenv_2024_116271 crossref_primary_10_1007_s40572_019_00229_5 crossref_primary_10_1016_j_ecoenv_2024_116030 crossref_primary_10_3390_ijerph20032321 crossref_primary_10_1016_j_apr_2024_102391 crossref_primary_10_1097_HJH_0000000000003795 crossref_primary_10_1016_j_scitotenv_2024_172985 crossref_primary_10_1007_s11356_020_08092_w crossref_primary_10_1016_j_envint_2024_108563 crossref_primary_10_1016_j_envint_2024_108562 crossref_primary_10_1007_s12011_025_04585_2 crossref_primary_10_1016_j_ecoenv_2023_114803 crossref_primary_10_1016_j_envres_2021_111019 crossref_primary_10_1097_EE9_0000000000000171 crossref_primary_10_1016_j_scitotenv_2024_177192 crossref_primary_10_1002_mco2_533 crossref_primary_10_1016_j_envres_2022_114866 crossref_primary_10_1136_bmjopen_2022_070772 crossref_primary_10_1016_j_ecoenv_2024_116283 crossref_primary_10_1016_j_envres_2024_119426 crossref_primary_10_3390_ijerph19063309 crossref_primary_10_1016_j_scitotenv_2022_160129 crossref_primary_10_1097_EE9_0000000000000147 crossref_primary_10_1016_j_envint_2021_106734 crossref_primary_10_3389_fpubh_2023_1138811 crossref_primary_10_1016_j_scitotenv_2018_09_088 crossref_primary_10_1186_s12940_017_0310_9 crossref_primary_10_1002_bimj_201800259 crossref_primary_10_1007_s11356_021_15589_5 crossref_primary_10_3390_ijerph19137819 crossref_primary_10_1186_s12889_025_21417_z crossref_primary_10_1016_j_envpol_2021_117606 crossref_primary_10_1186_s12889_024_21000_y crossref_primary_10_3390_toxics12030222 crossref_primary_10_1016_j_ajcnut_2023_05_020 crossref_primary_10_1016_j_psychres_2024_115986 crossref_primary_10_1021_acs_est_9b02226 crossref_primary_10_1186_s12905_023_02381_5 crossref_primary_10_1038_s41598_023_42282_z crossref_primary_10_1016_j_envpol_2020_115138 |
| Cites_doi | 10.1097/EDE.0b013e3181cc86e8 10.1097/01.ede.0000164811.25760.f1 10.1016/j.annepidem.2011.11.004 10.1016/j.scitotenv.2013.10.118 10.1289/ehp.1003099 10.1198/016214506000000735 10.1164/rccm.201009-1520ED 10.1126/science.1192603 10.1080/00401706.1970.10488634 10.1093/ije/dyr236 10.1111/j.1539-6924.2006.00770.x 10.1007/b98818 10.1111/j.1467-9868.2010.00740.x 10.1158/1055-9965.EPI-05-0456 10.1097/EDE.0b013e3181ce946c 10.1016/j.atmosenv.2005.10.022 10.1111/j.1467-9868.2005.00503.x 10.1111/ppe.12040 10.1021/es202340b 10.1289/ehp.1206367 10.1002/em.21767 10.1007/978-0-387-84858-7 10.1186/1742-7622-5-2 10.18637/jss.v033.i01 10.1111/j.2517-6161.1996.tb02080.x |
| ContentType | Journal Article |
| Copyright | 2014 International Biometric Society International Biometric Society 2014 COPYRIGHT 2015 Springer |
| Copyright_xml | – notice: 2014 International Biometric Society – notice: International Biometric Society 2014 – notice: COPYRIGHT 2015 Springer |
| DBID | AAYXX CITATION NPM ISR 7S9 L.6 7X8 5PM |
| DOI | 10.1007/s13253-014-0180-3 |
| DatabaseName | CrossRef PubMed Gale In Context: Science AGRICOLA AGRICOLA - Academic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed AGRICOLA AGRICOLA - Academic MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic AGRICOLA |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture Biology Environmental Sciences Medicine Statistics |
| EISSN | 1537-2693 |
| EndPage | 120 |
| ExternalDocumentID | PMC6261506 A432511311 30505142 10_1007_s13253_014_0180_3 26452934 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NIEHS NIH HHS grantid: R01 ES021482 – fundername: NIEHS NIH HHS grantid: T32 ES007334 |
| GroupedDBID | 06D 0R~ 0VY 199 1N0 203 2AX 2JN 2KG 2LR 2XV 30V 4.4 406 408 40D 40E 5GY 8UJ 95. 96X A8Z AABHQ AACDK AAHBH AAHNG AAIAL AAJBT AAJKR AANZL AAPKM AARTL AASML AATNV AATVU AAUYE AAWCG AAWIL AAYIU AAYQN AAYTO AAYZH AAZMS ABAKF ABAWQ ABBHK ABBRH ABDBE ABDBF ABDZT ABECU ABFAN ABFSG ABFTV ABHLI ABJNI ABJOX ABKCH ABMNI ABMQK ABNWP ABQBU ABQDR ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABXPI ABXSQ ABYWD ACAOD ACDIW ACDTI ACGFS ACHJO ACHSB ACHXU ACKNC ACMDZ ACMLO ACMTB ACOKC ACPIV ACPRK ACSTC ACTMH ACUHS ACZOJ ADHHG ADHIR ADKNI ADKPE ADODI ADRFC ADTPH ADULT ADURQ ADYFF ADZKW AEBTG AEFQL AEGNC AEJHL AEJRE AEKMD AELLO AEMSY AENEX AEOHA AEPYU AESKC AETCA AEUPB AEVLU AEXYK AEZWR AFBBN AFDZB AFHIU AFLOW AFOHR AFQWF AFRAH AFVYC AFWTZ AFZKB AGAYW AGDGC AGJBK AGLNM AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHWEU AHYZX AIAKS AIGIU AIHAF AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ AKBRZ ALMA_UNASSIGNED_HOLDINGS ALRMG AMKLP AMXSW AMYLF AMYQR ANMIH AOCGG ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYJHY AZFZN BAPOH BGNMA CSCUP D0L DDRTE DNIVK DPUIP DQDLB DSRWC DU5 EBD EBLON EBS ECEWR EIOEI EJD ESBYG F5P FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE GGCAI GGRSB GJIRD GNWQR GQ7 GXS H13 HF~ HMJXF HQ6 HRMNR IAO IEP IGS IKXTQ IPSME ISR ITM IWAJR I~Z J-C J0Z JAA JAAYA JBMMH JBSCW JBZCM JENOY JHFFW JKQEH JLEZI JLXEF JMS JPL JST JZLTJ KOV LLZTM M4Y ML. NPVJJ NQJWS NU0 O93 O9J P2P P9R PT4 R9I ROL RSV S27 S3B SA0 SHX SISQX SJN SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TN5 TSG U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W48 WK8 Z45 ZMTXR ~KM -5D -5G -BR -EM -~C 0VX 2VQ 3-Y AAKYL AARHV ABULA ACBXY ADINQ AELPN AI. AS~ BHOJU CAG COF FEDTE GIFXF GQ6 HGD HVGLF HZ~ IFM JSODD O9- PKN RNS S1Z UQL VH1 Z7U Z7W Z7Y Z7Z Z81 AAYXX ADXHL CITATION NPM AEIIB 7S9 L.6 7X8 5PM |
| ID | FETCH-LOGICAL-c706t-6f6ab39cd20213a87c3e6d5bc284eadbb5a36a85357d6d03a15e675fd2b5ef803 |
| IEDL.DBID | U2A |
| ISSN | 1085-7117 |
| IngestDate | Thu Aug 21 14:10:59 EDT 2025 Fri Jul 11 06:12:52 EDT 2025 Thu Jul 10 23:09:14 EDT 2025 Tue Jun 10 20:27:26 EDT 2025 Fri Jun 27 04:15:11 EDT 2025 Thu Apr 03 07:06:13 EDT 2025 Tue Jul 01 01:51:33 EDT 2025 Thu Apr 24 23:08:04 EDT 2025 Fri Feb 21 02:33:22 EST 2025 Sun Aug 31 12:00:39 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | WQS Correlation Nonlinear model Subset selection Variable selection |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c706t-6f6ab39cd20213a87c3e6d5bc284eadbb5a36a85357d6d03a15e675fd2b5ef803 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/6261506 |
| PMID | 30505142 |
| PQID | 1710221471 |
| PQPubID | 24069 |
| PageCount | 21 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_6261506 proquest_miscellaneous_2149011525 proquest_miscellaneous_1710221471 gale_infotracacademiconefile_A432511311 gale_incontextgauss_ISR_A432511311 pubmed_primary_30505142 crossref_citationtrail_10_1007_s13253_014_0180_3 crossref_primary_10_1007_s13253_014_0180_3 springer_journals_10_1007_s13253_014_0180_3 jstor_primary_26452934 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2015-03-01 |
| PublicationDateYYYYMMDD | 2015-03-01 |
| PublicationDate_xml | – month: 03 year: 2015 text: 2015-03-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Boston |
| PublicationPlace_xml | – name: Boston – name: Switzerland |
| PublicationTitle | Journal of agricultural, biological, and environmental statistics |
| PublicationTitleAbbrev | JABES |
| PublicationTitleAlternate | J Agric Biol Environ Stat |
| PublicationYear | 2015 |
| Publisher | Springer Science+Business Media, LLC Springer US Springer |
| Publisher_xml | – name: Springer Science+Business Media, LLC – name: Springer US – name: Springer |
| References | Hoerl, Kennard (CR14) 1970; 12 Meinshausen, Buhlmann (CR18) 2010; 72 Nocedal, Wright (CR19) 2006 Schecter, Lorber, Guo, Wu, Yun, Kannan, Hommel, Imran, Hynan, Cheng, Colacino, Birnbaum (CR24) 2013; 121 Wild (CR28) 2005; 14 Buck Louis, Yeung, Sundaram, Laughon, Zhang (CR4) 2013; 27 Breiman (CR2) 1996; 24 Wormuth, Scheringer, Vollenweider, Hungerbuhler (CR30) 2006; 26 CR12 CR10 Zou (CR31) 2006; 101 Zou, Hastie (CR32) 2005; 67 Vedal, Kaufman (CR27) 2011; 183 Billionnet, Sherrill, Annesi-Maesano (CR1) 2012; 22 Colt, Severson, Lubin, Rothman, Camann, Davis, Cerhan, Cozen, Hartge (CR7) 2005; 16 CR3 CR6 CR5 Kim, Kang, Lee, Lee, Jo, Kwak, Kim, Koh, Kho, Kim, Choi (CR15) 2014; 472 Mustapha, Blangiardo, Briggs, Hansell (CR17) 2011; 119 Roberts, Martin (CR22) 2006; 40 Tibshirani (CR25) 1996; 58 CR9 CR26 CR23 Leblanc, Tibshirani (CR16) 1993; 91 CR20 Gennings, Sabo, Carney (CR11) 2010; 21 Harville (CR13) 1997 Dominici, Peng, Barr, Bell (CR8) 2010; 21 Rappaport, Smith (CR21) 2010; 330 Wild (CR29) 2012; 41 L Breiman (180_CR2) 1996; 24 S Kim (180_CR15) 2014; 472 J Nocedal (180_CR19) 2006 H Zou (180_CR31) 2006; 101 J Colt (180_CR7) 2005; 16 R Tibshirani (180_CR25) 1996; 58 A Schecter (180_CR24) 2013; 121 SM Rappaport (180_CR21) 2010; 330 180_CR10 DA Harville (180_CR13) 1997 BA Mustapha (180_CR17) 2011; 119 CP Wild (180_CR28) 2005; 14 180_CR12 C Billionnet (180_CR1) 2012; 22 180_CR6 180_CR5 C Gennings (180_CR11) 2010; 21 N Meinshausen (180_CR18) 2010; 72 GM Buck Louis (180_CR4) 2013; 27 S Vedal (180_CR27) 2011; 183 M Wormuth (180_CR30) 2006; 26 F Dominici (180_CR8) 2010; 21 180_CR9 S Roberts (180_CR22) 2006; 40 M Leblanc (180_CR16) 1993; 91 180_CR26 CP Wild (180_CR29) 2012; 41 180_CR20 H Zou (180_CR32) 2005; 67 180_CR3 AE Hoerl (180_CR14) 1970; 12 180_CR23 |
| References_xml | – volume: 24 start-page: 49 year: 1996 end-page: 64 ident: CR2 article-title: Stacked regressions publication-title: Machine Learning – volume: 21 start-page: 187 issue: 2 year: 2010 end-page: 194 ident: CR8 article-title: Protecting human health from air pollution: shifting from a single-pollutant to a multipollutant approach publication-title: Epidemiology. doi: 10.1097/EDE.0b013e3181cc86e8 – volume: 58 start-page: 267 issue: 1 year: 1996 end-page: 288 ident: CR25 article-title: Regression Shrinkage and Selection via the Lasso publication-title: Journal of the Royal Statistical Society. – year: 2006 ident: CR19 publication-title: Numerical optimization – volume: 16 start-page: 516 issue: 4 year: 2005 end-page: 525 ident: CR7 article-title: Organochlorines in carpet dust and non-Hodgkin lymphoma publication-title: Epidemiology doi: 10.1097/01.ede.0000164811.25760.f1 – volume: 22 start-page: 126 issue: 2 year: 2012 end-page: 141 ident: CR1 article-title: Estimating the health effects of exposure to multi-pollutant mixture publication-title: Annals of Epidemiology doi: 10.1016/j.annepidem.2011.11.004 – ident: CR12 – volume: 472 start-page: 49 year: 2014 end-page: 55 ident: CR15 article-title: Urinary phthalate metabolites among elementary school children of Korea: sources, risks, and their association with oxidative stress marker publication-title: Sci Total Environment doi: 10.1016/j.scitotenv.2013.10.118 – ident: CR10 – volume: 119 start-page: 1478 year: 2011 end-page: 1482 ident: CR17 article-title: Traffic ari pollution and other risk factors for respiratory illness in schoolchildren in the Niger-Delta region of Nigeria publication-title: Environ Health Perspect. doi: 10.1289/ehp.1003099 – volume: 121 start-page: 473 issue: 4 year: 2013 end-page: 494 ident: CR24 article-title: Phthalate concentrations and dietary exposure from food purchased in New York State publication-title: Environ Health Perspect. – ident: CR6 – volume: 101 start-page: 1418 year: 2006 end-page: 1429 ident: CR31 article-title: The adaptive lasso and its oracle properties publication-title: J American Statistical Association. doi: 10.1198/016214506000000735 – volume: 183 start-page: 4 issue: 1 year: 2011 end-page: 6 ident: CR27 article-title: What does multi-pollutant air pollution research mean? publication-title: Am J Respir Crit Care Med. doi: 10.1164/rccm.201009-1520ED – volume: 91 start-page: 1641 year: 1993 end-page: 1650 ident: CR16 article-title: Combining estimates in regression and classification publication-title: J American Statistical Association – ident: CR23 – volume: 330 start-page: 460 issue: 6003 year: 2010 end-page: 461 ident: CR21 article-title: Epidemiology. Environment and disease risks publication-title: Science. doi: 10.1126/science.1192603 – volume: 12 start-page: 55 issue: 1 year: 1970 end-page: 67 ident: CR14 article-title: Ridge regression: biased estimation for nonorthogonal problems publication-title: Technometrics doi: 10.1080/00401706.1970.10488634 – volume: 41 start-page: 24 issue: 1 year: 2012 end-page: 32 ident: CR29 article-title: The exposome: from concept to utility publication-title: International journal of epidemiology. doi: 10.1093/ije/dyr236 – volume: 26 start-page: 803 issue: 3 year: 2006 end-page: 824 ident: CR30 article-title: What are the sources of exposure to eight frequently used phthalic acid esters in Europeans? publication-title: Risk Analysis doi: 10.1111/j.1539-6924.2006.00770.x – year: 1997 ident: CR13 publication-title: Matrix algebra from a statistician’s perspective doi: 10.1007/b98818 – ident: CR3 – volume: 72 start-page: 417 year: 2010 end-page: 473 ident: CR18 article-title: Stability selection publication-title: Journal of the Royal Statistical Society doi: 10.1111/j.1467-9868.2010.00740.x – ident: CR9 – ident: CR5 – volume: 14 start-page: 1847 issue: 8 year: 2005 end-page: 1850 ident: CR28 article-title: Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology publication-title: Cancer Epidemiol Biomarkers Prev. doi: 10.1158/1055-9965.EPI-05-0456 – volume: 21 start-page: S77 year: 2010 end-page: S84 ident: CR11 article-title: Identifying subsets of complex mixtures most associated with complex diseases polychlorinated biphenyls and endometriosis as a case study publication-title: Epidemiology doi: 10.1097/EDE.0b013e3181ce946c – volume: 40 start-page: 984 issue: 5 year: 2006 end-page: 991 ident: CR22 article-title: Investigating the mixture of air pollutants associated with adverse health outcomes publication-title: Atmospheric Environment doi: 10.1016/j.atmosenv.2005.10.022 – ident: CR26 – volume: 67 start-page: 301 issue: 2 year: 2005 end-page: 320 ident: CR32 article-title: Regularization and variable selection via the elastic net publication-title: Journal of the Royal Statistical Society. Series B (Statistical Methodology) doi: 10.1111/j.1467-9868.2005.00503.x – ident: CR20 – volume: 27 start-page: 229 issue: 3 year: 2013 end-page: 236 ident: CR4 article-title: The exposome-exciting opportunities for discoveries in reproductive and perinatal epidemiology publication-title: Paediatr Perinat Epidemiol doi: 10.1111/ppe.12040 – ident: 180_CR20 – ident: 180_CR6 – volume: 101 start-page: 1418 year: 2006 ident: 180_CR31 publication-title: J American Statistical Association. doi: 10.1198/016214506000000735 – volume: 27 start-page: 229 issue: 3 year: 2013 ident: 180_CR4 publication-title: Paediatr Perinat Epidemiol doi: 10.1111/ppe.12040 – volume: 72 start-page: 417 year: 2010 ident: 180_CR18 publication-title: Journal of the Royal Statistical Society doi: 10.1111/j.1467-9868.2010.00740.x – ident: 180_CR9 doi: 10.1021/es202340b – volume: 121 start-page: 473 issue: 4 year: 2013 ident: 180_CR24 publication-title: Environ Health Perspect. doi: 10.1289/ehp.1206367 – volume-title: Numerical optimization year: 2006 ident: 180_CR19 – volume: 16 start-page: 516 issue: 4 year: 2005 ident: 180_CR7 publication-title: Epidemiology doi: 10.1097/01.ede.0000164811.25760.f1 – volume: 24 start-page: 49 year: 1996 ident: 180_CR2 publication-title: Machine Learning – volume-title: Matrix algebra from a statistician’s perspective year: 1997 ident: 180_CR13 doi: 10.1007/b98818 – ident: 180_CR3 doi: 10.1002/em.21767 – volume: 14 start-page: 1847 issue: 8 year: 2005 ident: 180_CR28 publication-title: Cancer Epidemiol Biomarkers Prev. doi: 10.1158/1055-9965.EPI-05-0456 – volume: 21 start-page: 187 issue: 2 year: 2010 ident: 180_CR8 publication-title: Epidemiology. doi: 10.1097/EDE.0b013e3181cc86e8 – ident: 180_CR12 doi: 10.1007/978-0-387-84858-7 – ident: 180_CR5 – volume: 67 start-page: 301 issue: 2 year: 2005 ident: 180_CR32 publication-title: Journal of the Royal Statistical Society. Series B (Statistical Methodology) doi: 10.1111/j.1467-9868.2005.00503.x – volume: 12 start-page: 55 issue: 1 year: 1970 ident: 180_CR14 publication-title: Technometrics doi: 10.1080/00401706.1970.10488634 – volume: 119 start-page: 1478 year: 2011 ident: 180_CR17 publication-title: Environ Health Perspect. doi: 10.1289/ehp.1003099 – ident: 180_CR26 doi: 10.1186/1742-7622-5-2 – volume: 21 start-page: S77 year: 2010 ident: 180_CR11 publication-title: Epidemiology doi: 10.1097/EDE.0b013e3181ce946c – volume: 41 start-page: 24 issue: 1 year: 2012 ident: 180_CR29 publication-title: International journal of epidemiology. doi: 10.1093/ije/dyr236 – ident: 180_CR10 doi: 10.18637/jss.v033.i01 – volume: 472 start-page: 49 year: 2014 ident: 180_CR15 publication-title: Sci Total Environment doi: 10.1016/j.scitotenv.2013.10.118 – volume: 330 start-page: 460 issue: 6003 year: 2010 ident: 180_CR21 publication-title: Science. doi: 10.1126/science.1192603 – ident: 180_CR23 – volume: 40 start-page: 984 issue: 5 year: 2006 ident: 180_CR22 publication-title: Atmospheric Environment doi: 10.1016/j.atmosenv.2005.10.022 – volume: 26 start-page: 803 issue: 3 year: 2006 ident: 180_CR30 publication-title: Risk Analysis doi: 10.1111/j.1539-6924.2006.00770.x – volume: 22 start-page: 126 issue: 2 year: 2012 ident: 180_CR1 publication-title: Annals of Epidemiology doi: 10.1016/j.annepidem.2011.11.004 – volume: 91 start-page: 1641 year: 1993 ident: 180_CR16 publication-title: J American Statistical Association – volume: 58 start-page: 267 issue: 1 year: 1996 ident: 180_CR25 publication-title: Journal of the Royal Statistical Society. doi: 10.1111/j.2517-6161.1996.tb02080.x – volume: 183 start-page: 4 issue: 1 year: 2011 ident: 180_CR27 publication-title: Am J Respir Crit Care Med. doi: 10.1164/rccm.201009-1520ED |
| SSID | ssj0013602 |
| Score | 2.5914812 |
| Snippet | In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and... |
| SourceID | pubmedcentral proquest gale pubmed crossref springer jstor |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 100 |
| SubjectTerms | Actors Agriculture Biostatistics Chemical properties Health Sciences inflation Inflation (Economics) Mathematics and Statistics Medicine Monitoring/Environmental Analysis regression analysis Risk assessment shrinkage Statistics Statistics for Life Sciences variance |
| Title | Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting |
| URI | https://www.jstor.org/stable/26452934 https://link.springer.com/article/10.1007/s13253-014-0180-3 https://www.ncbi.nlm.nih.gov/pubmed/30505142 https://www.proquest.com/docview/1710221471 https://www.proquest.com/docview/2149011525 https://pubmed.ncbi.nlm.nih.gov/PMC6261506 |
| Volume | 20 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwED_BJtB4QBAoCx-TQUhIoEhJHOfjMSodAwQSLRXjybITpysrKVrah_333CVOq8KGxHPOtpI7n8-53_0O4KXmScWDUHm6iBIv0iLztK6MF1bazwqT4YnUoi0-xyfT6MOpOLV13E2Pdu9Tkq2n3ha78VAQ9odQEyk6j5uwL_DqTjiuaZhvUwexBRqmwkuCIOlTmVdNsXMYWZfcwRKvCjj_xk3-kTxtz6Tje3DXBpMs77R_H26Y2oE7-ezCEmoYB251zSYvHRiMtjVtOMhu6saB259sft2BA4o9O-rmB_BjuCFz7mo12bJi39pfqaZkX9aoE3QpbLL-ycZm1uFpa4ZBMCPwyOKSDanzx0KR9Fu1UmxeM8XG8-ac9VQobGJa3PVDmB6Pvg5PPNuawSsSP155cRUrzbOiRAUEXKVJwU1cCl3gaYe2qbVQPFYYCoikjEufq0AYvJpUZaiFqVKfD2CvXtbmEFiUlBGGRbzkcRalVZyh7y4zqukuUpy8dMHvdSQLy1tO7TMWcsu4TGqVqFZJapXchdebIb860o5_Cb8gxUsiw6gJbTNT66aR7ydjmUecbmA8CFx4ZYWqJS5eKFu8gK9A_Fk7koPWgDYLh5Q6znjkwvPeoiRuYcrLqNos140MKMqjflHB9TL4mIqERShceNRZ4WYFTt0Igyh0Idmxz40AUYjvPqnnZy2VOF5niWLShTe9JUvrw5rrv9jj_5J-AgcYZIoOt_cU9lYXa_MMA7mVPoL9_N33j6OjdgP_Bp82Pq8 |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB6VIqAcEAQC5rkgEBLIUuz1-nHgEKWtEvqQSFu1t2XXXodAcFCdCOX38EeZsdeJAi0Sh5499tqe8cys55tvAF5rHuXc85Wr0yByAy0SV-vcuH6uO0lqEoxIFdriMOyfBB_PxNkG_Gp6YSq0e1OSrDz1qtmN-4KwP4SaiNF5WCTlnln8xH1a-WGwjUp94_u7O8e9vmtHCbhp1AlnbpiHSvMkzXCv73EVRyk3YSZ0it4Z36XWQvFQYegSURZmHa48YTCVzjNfC5PHHY7XvQbXMfeI6dM58burUkVogY2xcCPPi5rS6UW3vBb8bAioYZAXJbh_4zT_KNZWMXD3LtyxySvr1tZ2DzZM0YLb3dG5JfAwLbhRD7dctKC9s-qhw5OsEylbcPPA1vNbsEW5bk0VfR--9pbk0XVvKJvm7LT6dWsy9mmONoAujB3Nv7OhGdX43YJh0s0IrDJZsB5NGpkokt5WM8XGBVNsOC6_sYZ6hR2ZCuf9AE6uRH9t2CymhXkELIiyANMwnvEwCeI8TDBWZAn1kKcxXjxzoNPoSKaWJ53GdUzkiuGZ1CpRrZLUKrkD75an_KhJQv4l_IoUL4l8oyB0z0jNy1IOjoayG3Da8XHPc-CtFcqnuHiqbLMEPgLxda1JtisDWi7sU6k64YEDLxuLkugyqA6kCjOdl9KjrJLmU3mXy-BhakoWvnDgYW2FyxU4TT_0At-BaM0-lwJEWb5-pBh_qajLcftMlJYOvG8sWVqfWV7-xh7_l_QLuNU_PtiX-4PDvSewhQmuqDGDT2Fzdj43zzCJnOnn1UfM4PNVe43foJR69g |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9NADLbGENN4QFAohJ8HAiGBojW5XNI88FC1qzYGE7RU7O24Sy6lUNJpaYX6V_EvYieXVoUNiYc9x8klsWP74s-fAZ5rHmXc85WrkyByAy1iV-vMuH6mW3FiYoxIJdriODwYBW9PxMkW_Kp7YUq0e12SrHoaiKUpn--dptneuvGN-4JwQISgaKMjsajKI7P8iXu24s1hDxX8wvf7-5-6B64dK-AmUSucu2EWKs3jJMV9v8dVO0q4CVOhE_TU-F61FoqHCsOYiNIwbXHlCYNpdZb6Wpis3eJ43StwNaDmY_yARn5nXbYILcixLdzI86K6jHreLW8EQhsOKkjkecnu35jNPwq3ZTzs34QbNpFlncrybsGWyRtwvTM-s2QepgHXqkGXywY099f9dHiSdShFA3be29p-A3Yp761oo2_Dt-6KSLrqE2WzjH0uf-OalH1coD2gO2PDxQ82MOMKy5szTMAZAVemS9alqSNTRdI9NVdskjPFBpPiO6tpWNjQlJjvOzC6FP01YTuf5eYesCBKA0zJeMrDOGhnYYxxI42pnzxp48VTB1q1jmRiOdNpdMdUrtmeSa0S1SpJrZI78Gp1ymlFGPIv4WekeElEHDkhfcZqURTycDiQnYDT7o97ngMvrVA2w8UTZRsn8BGIu2tDslka0Gphn8rWMQ8ceFpblET3QTUhlZvZopAeZZg0q8q7WAYPU4Oy8IUDdysrXK3AaRKiF_gORBv2uRIg-vLNI_nka0ljjltpord04HVtydL6z-LiN3b_v6SfwM6HXl--Ozw-egC7mOuKCj74ELbnZwvzCPPJuX5cfsMMvly20_gN9ep_KQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Characterization+of+Weighted+Quantile+Sum+Regression+for+Highly+Correlated+Data+in+a+Risk+Analysis+Setting&rft.jtitle=Journal+of+agricultural%2C+biological%2C+and+environmental+statistics&rft.au=Carrico%2C+Caroline&rft.au=Gennings%2C+Chris&rft.au=Wheeler%2C+David+C.&rft.au=Factor-Litvak%2C+Pam&rft.date=2015-03-01&rft.pub=Springer+US&rft.issn=1085-7117&rft.eissn=1537-2693&rft.volume=20&rft.issue=1&rft.spage=100&rft.epage=120&rft_id=info:doi/10.1007%2Fs13253-014-0180-3&rft.externalDocID=10_1007_s13253_014_0180_3 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1085-7117&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1085-7117&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1085-7117&client=summon |