The Rational SPDE Approach for Gaussian Random Fields With General Smoothness
A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form , where is Gaussian white noise, L is a second-order differential operator, and is a parameter that determines the...
Saved in:
Published in | Journal of computational and graphical statistics Vol. 29; no. 2; pp. 274 - 285 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Alexandria
Taylor & Francis
02.04.2020
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
ISSN | 1061-8600 1537-2715 1537-2715 |
DOI | 10.1080/10618600.2019.1665537 |
Cover
Abstract | A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form
, where
is Gaussian white noise, L is a second-order differential operator, and
is a parameter that determines the smoothness of u. However, this approach has been limited to the case
, which excludes several important models and makes it necessary to keep β fixed during inference. We propose a new method, the rational SPDE approach, which in spatial dimension
is applicable for any
, and thus remedies the mentioned limitation. The presented scheme combines a finite element discretization with a rational approximation of the function
to approximate u. For the resulting approximation, an explicit rate of convergence to u in mean-square sense is derived. Furthermore, we show that our method has the same computational benefits as in the restricted case
. Several numerical experiments and a statistical application are used to illustrate the accuracy of the method, and to show that it facilitates likelihood-based inference for all model parameters including β.
Supplementary materials
for this article are available online. |
---|---|
AbstractList | A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form , where is Gaussian white noise, L is a second-order differential operator, and is a parameter that determines the smoothness of u. However, this approach has been limited to the case , which excludes several important models and makes it necessary to keep beta fixed during inference. We propose a new method, the rational SPDE approach, which in spatial dimension is applicable for any , and thus remedies the mentioned limitation. The presented scheme combines a finite element discretization with a rational approximation of the function to approximate u. For the resulting approximation, an explicit rate of convergence to u in mean-square sense is derived. Furthermore, we show that our method has the same computational benefits as in the restricted case . Several numerical experiments and a statistical application are used to illustrate the accuracy of the method, and to show that it facilitates likelihood-based inference for all model parameters including beta. for this article are available online. A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form , where is Gaussian white noise, L is a second-order differential operator, and is a parameter that determines the smoothness of u. However, this approach has been limited to the case , which excludes several important models and makes it necessary to keep β fixed during inference. We propose a new method, the rational SPDE approach, which in spatial dimension is applicable for any , and thus remedies the mentioned limitation. The presented scheme combines a finite element discretization with a rational approximation of the function to approximate u. For the resulting approximation, an explicit rate of convergence to u in mean-square sense is derived. Furthermore, we show that our method has the same computational benefits as in the restricted case . Several numerical experiments and a statistical application are used to illustrate the accuracy of the method, and to show that it facilitates likelihood-based inference for all model parameters including β. Supplementary materials for this article are available online. A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form , where is Gaussian white noise, L is a second-order differential operator, and is a parameter that determines the smoothness of u. However, this approach has been limited to the case , which excludes several important models and makes it necessary to keep β fixed during inference. We propose a new method, the rational SPDE approach, which in spatial dimension is applicable for any , and thus remedies the mentioned limitation. The presented scheme combines a finite element discretization with a rational approximation of the function to approximate u. For the resulting approximation, an explicit rate of convergence to u in mean-square sense is derived. Furthermore, we show that our method has the same computational benefits as in the restricted case . Several numerical experiments and a statistical application are used to illustrate the accuracy of the method, and to show that it facilitates likelihood-based inference for all model parameters including β.Supplementary materials for this article are available online. |
Author | Kirchner, Kristin Bolin, David |
Author_xml | – sequence: 1 givenname: David orcidid: 0000-0003-2361-5465 surname: Bolin fullname: Bolin, David email: david.bolin@chalmers.se organization: Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg – sequence: 2 givenname: Kristin orcidid: 0000-0002-3609-9431 surname: Kirchner fullname: Kirchner, Kristin organization: Seminar for Applied Mathematics, ETH Zürich |
BackLink | https://gup.ub.gu.se/publication/286210$$DView record from Swedish Publication Index https://research.chalmers.se/publication/514046$$DView record from Swedish Publication Index |
BookMark | eNqFkVFr1UAQhYNUsK3-BCHgc647m91sgi-W2l6FimIrPg6TZNJsSbJxN6H037vxti-C-jTD8J3DcM5JcjS5iZPkNYgdiFK8BVFAWQixkwKqHRSF1rl5lhxDHJk0oI_iHplsg14kJyHcCSGgqMxx8vmm5_QbLdZNNKTXXz9cpGfz7B01fdo5n-5pDcHSFJmpdWN6aXloQ_rDLn2654n9phqdW_qJQ3iZPO9oCPzqcZ4m3y8vbs4_Zldf9p_Oz66yRlXVkmlgXYJkgpyNIFWpQkthBHDB2khoQUliYNMqaYiozGVbk4YG8qrTbZmfJtcH33DP81rj7O1I_gEdWfQcmHzTY9PTMLIPGBgLUSoh6xZlDQZVrRRSXgFCXkIJNbS1ouia_dX1dp0xnm7XzU2WhQQR-TcHPub1c-Ww4J1bfcwxoFRQVWByoSP17kA13oXgucPGLr8DXzzZAUHg1iI-tYhbi_jYYlTrP9RPX_1P9_6gs1OscaR754cWF3oYnO88TY0NmP_b4hfWTLNr |
CitedBy_id | crossref_primary_10_1007_s11222_022_10136_9 crossref_primary_10_1016_j_spasta_2022_100591 crossref_primary_10_1016_j_spasta_2023_100750 crossref_primary_10_1080_10618600_2023_2231051 crossref_primary_10_3390_math12182899 crossref_primary_10_1016_j_cma_2024_117146 crossref_primary_10_1137_21M144788X crossref_primary_10_1016_j_spasta_2022_100599 crossref_primary_10_3150_22_BEJ1507 crossref_primary_10_1214_21_STS838 crossref_primary_10_1137_23M1567035 crossref_primary_10_3150_23_BEJ1647 crossref_primary_10_1007_s13253_024_00602_4 crossref_primary_10_1111_tbed_14627 crossref_primary_10_1177_10943420241261981 crossref_primary_10_3150_20_BEJ1317 crossref_primary_10_1007_s11222_024_10448_y crossref_primary_10_1016_j_cma_2021_114166 crossref_primary_10_1214_21_BA1283 crossref_primary_10_1214_22_BA1342 crossref_primary_10_1137_23M1624749 crossref_primary_10_1016_j_spasta_2024_100847 crossref_primary_10_1111_rssc_12405 crossref_primary_10_1680_jgeot_22_00316 crossref_primary_10_1142_S0218202520500050 crossref_primary_10_1137_22M1494397 crossref_primary_10_1007_s10444_024_10187_8 crossref_primary_10_1007_s00158_023_03716_4 crossref_primary_10_1007_s40072_023_00316_7 crossref_primary_10_1016_j_cma_2023_116358 crossref_primary_10_1016_j_jaridenv_2023_105051 crossref_primary_10_1007_s44007_023_00077_8 crossref_primary_10_51387_25_NEJSDS78 crossref_primary_10_1007_s00366_023_01819_6 crossref_primary_10_1007_s10543_023_00986_8 crossref_primary_10_1090_mcom_3929 crossref_primary_10_1002_env_2610 crossref_primary_10_1137_21M1458880 crossref_primary_10_1111_sjos_12555 crossref_primary_10_1137_22M1529907 crossref_primary_10_1007_s13540_024_00256_6 crossref_primary_10_1137_21M1400717 crossref_primary_10_1007_s10543_018_0719_8 crossref_primary_10_2139_ssrn_4126798 crossref_primary_10_1016_j_spasta_2024_100867 crossref_primary_10_1088_1361_6420_ac3994 crossref_primary_10_1007_s00466_023_02424_6 crossref_primary_10_1214_24_STS923 crossref_primary_10_1515_cmam_2022_0237 crossref_primary_10_1016_j_cma_2021_114014 crossref_primary_10_1016_j_mechmat_2023_104821 crossref_primary_10_1002_wics_1512 crossref_primary_10_1016_j_probengmech_2022_103203 |
Cites_doi | 10.1002/wics.1443 10.1016/j.spasta.2019.01.002 10.1111/j.1467-9868.2008.00700.x 10.1198/016214504000000241 10.1080/10618600.2014.914946 10.1070/SM1977v032n04ABEH002404 10.1002/nla.2167 10.1080/01621459.2015.1044091 10.1093/imanum/dry091 10.1111/j.1467-9868.2011.01007.x 10.1029/2009EO360002 10.1111/sjos.12141 10.1201/9780203492024 10.1198/106186006X132178 10.1080/01621459.2015.1123632 10.1111/sjos.12297 10.1007/978-1-4612-1494-6 10.1007/s10543-018-0719-8 10.1017/CBO9780511623721 10.1111/j.1467-9868.2011.00777.x 10.1137/1.9781611972030 10.18637/jss.v063.i19 10.1214/14-STS487 10.1090/mcom/2960 10.1007/s13253-018-00348-w 10.5194/npg-24-481-2017 10.1090/S0025-5718-03-01590-4 10.1090/S0025-5718-2015-02937-8 10.1016/j.spasta.2015.10.001 10.1007/s10208-014-9208-x |
ContentType | Journal Article |
Copyright | 2019 The Author(s). Published with license by Taylor & Francis Group, LLC 2019 2019 The Author(s). Published with license by Taylor & Francis Group, LLC. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2019 The Author(s). Published with license by Taylor & Francis Group, LLC 2019 – notice: 2019 The Author(s). Published with license by Taylor & Francis Group, LLC. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 0YH AAYXX CITATION JQ2 ADTPV AOWAS F1U ABBSD D8T F1S ZZAVC |
DOI | 10.1080/10618600.2019.1665537 |
DatabaseName | Taylor & Francis Open Access CrossRef ProQuest Computer Science Collection SwePub SwePub Articles SWEPUB Göteborgs universitet SWEPUB Chalmers tekniska högskola full text SWEPUB Freely available online SWEPUB Chalmers tekniska högskola SwePub Articles full text |
DatabaseTitle | CrossRef ProQuest Computer Science Collection |
DatabaseTitleList | ProQuest Computer Science Collection |
Database_xml | – sequence: 1 dbid: 0YH name: Taylor & Francis Open Access url: https://www.tandfonline.com sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Statistics Mathematics |
EISSN | 1537-2715 |
EndPage | 285 |
ExternalDocumentID | oai_research_chalmers_se_608402bd_2b17_4b44_a391_138181b1db4a oai_gup_ub_gu_se_286210 10_1080_10618600_2019_1665537 1665537 |
Genre | Research Article |
GroupedDBID | -~X .4S .7F .DC .QJ 0BK 0R~ 0YH 30N 4.4 5GY AAENE AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABFAN ABFIM ABJNI ABLIJ ABLJU ABPAQ ABPEM ABTAI ABXUL ABXYU ABYWD ACGFO ACGFS ACIWK ACMTB ACTIO ACTMH ADCVX ADGTB AEGXH AELLO AENEX AEOZL AEPSL AEUPB AEYOC AFVYC AGDLA AGMYJ AHDZW AIAGR AIJEM AKBRZ AKBVH AKOOK ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH ARCSS AVBZW AWYRJ BLEHA CCCUG CS3 D0L DGEBU DKSSO DU5 EBS E~A E~B F5P GTTXZ H13 HF~ HZ~ H~P IAO IEA IGG IGS IOF IPNFZ J.P JAA KYCEM LJTGL M4Z MS~ NA5 NY~ O9- P2P PQQKQ RIG RNANH ROSJB RTWRZ RWL RXW S-T SNACF TAE TBQAZ TDBHL TEJ TFL TFT TFW TN5 TTHFI TUROJ TUS UT5 UU3 WZA XWC ZGOLN ~S~ AAGDL AAHIA AAYXX ADXHL ADYSH AFRVT AMPGV AMVHM CITATION JQ2 TASJS 07G 29K 2AX AAIKQ AAKBW AAWIL ABAWQ ABBHK ABQDR ABXSQ ACAGQ ACDIW ACGEE ACHJO ACTCW ADODI ADTPV ADULT AEUMN AGCQS AGLEN AGLNM AGROQ AHMOU AIHAF ALCKM ALRMG AMATQ AMEWO AMXXU AOWAS BCCOT BPLKW C06 CRFIH D-I DMQIW DQDLB DSRWC DWIFK ECEWR EJD F1U FEDTE GIFXF HGD HQ6 HVGLF IPSME IVXBP JAAYA JBMMH JBZCM JENOY JHFFW JKQEH JLEZI JLXEF JMS JPL JST NUSFT QCRFL RNS SA0 TAQ TFMCV TOXWX UB9 ABBSD D8T F1S ZZAVC |
ID | FETCH-LOGICAL-c499t-51e5812ea13e70a4946520701e6e5721d142ae1e7d427aaa832dba51c139f5d83 |
IEDL.DBID | 0YH |
ISSN | 1061-8600 1537-2715 |
IngestDate | Thu Aug 21 06:11:06 EDT 2025 Thu Aug 21 06:44:35 EDT 2025 Wed Aug 13 10:48:04 EDT 2025 Thu Apr 24 22:52:36 EDT 2025 Tue Jul 01 02:05:30 EDT 2025 Wed Dec 25 09:08:35 EST 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 2 |
Language | English |
License | open-access: http://creativecommons.org/licenses/by/4.0/: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.: http://creativecommons.org/licenses/by/4.0 |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c499t-51e5812ea13e70a4946520701e6e5721d142ae1e7d427aaa832dba51c139f5d83 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ORCID | 0000-0003-2361-5465 0000-0002-3609-9431 |
OpenAccessLink | https://www.tandfonline.com/doi/abs/10.1080/10618600.2019.1665537 |
PQID | 2419917305 |
PQPubID | 29738 |
PageCount | 12 |
ParticipantIDs | informaworld_taylorfrancis_310_1080_10618600_2019_1665537 swepub_primary_oai_research_chalmers_se_608402bd_2b17_4b44_a391_138181b1db4a crossref_citationtrail_10_1080_10618600_2019_1665537 crossref_primary_10_1080_10618600_2019_1665537 proquest_journals_2419917305 swepub_primary_oai_gup_ub_gu_se_286210 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2020-04-02 |
PublicationDateYYYYMMDD | 2020-04-02 |
PublicationDate_xml | – month: 04 year: 2020 text: 2020-04-02 day: 02 |
PublicationDecade | 2020 |
PublicationPlace | Alexandria |
PublicationPlace_xml | – name: Alexandria |
PublicationTitle | Journal of computational and graphical statistics |
PublicationYear | 2020 |
Publisher | Taylor & Francis Taylor & Francis Ltd |
Publisher_xml | – name: Taylor & Francis – name: Taylor & Francis Ltd |
References | R Core Team (CIT0028) 2017 CIT0032 CIT0031 CIT0012 CIT0034 CIT0011 CIT0033 Mearns L. O. (CIT0025) 2007 CIT0014 Rasmussen C. E. (CIT0029) 2006 CIT0036 Baker G. A. (CIT0001) 1996; 59 CIT0013 CIT0035 CIT0016 Remez E. Y. (CIT0030) 1934; 10 CIT0015 CIT0037 CIT0018 CIT0017 CIT0039 CIT0019 Matérn B. (CIT0023) 1960; 49 CIT0021 CIT0020 CIT0022 Whittle P. (CIT0038) 1963; 40 Driscoll T. A. (CIT0010) 2014 CIT0003 CIT0002 CIT0024 CIT0005 CIT0027 CIT0004 CIT0026 CIT0007 CIT0006 CIT0009 CIT0008 |
References_xml | – ident: CIT0002 doi: 10.1002/wics.1443 – ident: CIT0003 doi: 10.1016/j.spasta.2019.01.002 – volume-title: R: A Language and Environment for Statistical Computing year: 2017 ident: CIT0028 – ident: CIT0034 doi: 10.1111/j.1467-9868.2008.00700.x – ident: CIT0039 doi: 10.1198/016214504000000241 – ident: CIT0027 doi: 10.1080/10618600.2014.914946 – ident: CIT0032 doi: 10.1070/SM1977v032n04ABEH002404 – ident: CIT0016 doi: 10.1002/nla.2167 – ident: CIT0008 doi: 10.1080/01621459.2015.1044091 – ident: CIT0005 doi: 10.1093/imanum/dry091 – ident: CIT0035 doi: 10.1111/j.1467-9868.2011.01007.x – ident: CIT0004 – ident: CIT0024 doi: 10.1029/2009EO360002 – volume: 10 start-page: 41 year: 1934 ident: CIT0030 publication-title: Communications de la Societé Mathématique de Kharkov, – ident: CIT0037 doi: 10.1111/sjos.12141 – ident: CIT0033 doi: 10.1201/9780203492024 – volume-title: Chebfun Guide year: 2014 ident: CIT0010 – ident: CIT0012 doi: 10.1198/106186006X132178 – ident: CIT0019 doi: 10.1080/01621459.2015.1123632 – ident: CIT0031 doi: 10.1111/sjos.12297 – volume: 40 start-page: 974 year: 1963 ident: CIT0038 publication-title: Bulletin of the International Statistical Institute – ident: CIT0036 doi: 10.1007/978-1-4612-1494-6 – ident: CIT0006 doi: 10.1007/s10543-018-0719-8 – ident: CIT0009 doi: 10.1017/CBO9780511623721 – ident: CIT0022 doi: 10.1111/j.1467-9868.2011.00777.x – volume: 49 start-page: 144 year: 1960 ident: CIT0023 publication-title: Meddelanden Från Statens Skogsforskningsinstitut – ident: CIT0015 doi: 10.1137/1.9781611972030 – ident: CIT0021 doi: 10.18637/jss.v063.i19 – volume-title: Gaussian Processes for Machine Learning, Adaptive Computation and Machine Learning year: 2006 ident: CIT0029 – ident: CIT0014 doi: 10.1214/14-STS487 – ident: CIT0018 doi: 10.1090/mcom/2960 – ident: CIT0017 doi: 10.1007/s13253-018-00348-w – ident: CIT0020 doi: 10.5194/npg-24-481-2017 – ident: CIT0013 doi: 10.1090/S0025-5718-03-01590-4 – volume-title: The North American Regional Climate Change Assessment Program Dataset year: 2007 ident: CIT0025 – volume: 59 volume-title: Padé Approximants, Encyclopedia of Mathematics and Its Applications year: 1996 ident: CIT0001 – ident: CIT0007 doi: 10.1090/S0025-5718-2015-02937-8 – ident: CIT0011 doi: 10.1016/j.spasta.2015.10.001 – ident: CIT0026 doi: 10.1007/s10208-014-9208-x |
SSID | ssj0001697 |
Score | 2.5310783 |
Snippet | A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential... |
SourceID | swepub proquest crossref informaworld |
SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
StartPage | 274 |
SubjectTerms | Approximation approximations Fields (mathematics) Fractional operators markov random-fields Matematik Matern covariances Mathematical models Mathematical sciences Mathematics Matérn covariances Nonstationary Gaussian fields Operators (mathematics) Parameters Partial differential equations Smoothness Spatial statistics Statistical inference Stochastic partial differential equations Stochastic processes White noise |
Title | The Rational SPDE Approach for Gaussian Random Fields With General Smoothness |
URI | https://www.tandfonline.com/doi/abs/10.1080/10618600.2019.1665537 https://www.proquest.com/docview/2419917305 https://gup.ub.gu.se/publication/286210 https://research.chalmers.se/publication/514046 |
Volume | 29 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9xADLYoXOgBAW3V5aU5IG6hmVceRwQsK8QiVEC0p9FMMmErlV1Esv8fO5msWKkVBy45RHEedmx_Hnk-AxzaIrEqr3SUO00FCmK4zJZkEC99rr3smmjG18noXl3-0n03YR3aKqmGrjqiiDZWk3NbV_cdcT-oiskwUVNjVn7Mk0RrmX6CNYFAkbr64t-jRTDmYb4KikQk02_i-d9tltLTEnnpMgR9SyvapqLhJmwEDMlOOqNvwYqfbsPn8YKAtd6GdQKRHQfzFxjjv8B-hlU_dntzds5OApU4wyezCzuvaS8lXjMtZ09sSF1tNXv400xYoKVmt08ztCnFxa9wPzy_Ox1FYYxCVGA500Sae41p3FsufRqjZVSiBXo694nXWACWXAnruU9LJVJrLfp46azmBYLDSpeZ_Aar09nUfwfmEqVl5YTOXKqs0lmh4iKvYmnz1FcVH4DqtWeKwDFOoy7-Gh6oSHulG1K6CUofwPFC7Lkj2XhPIH9rGtO0qxtVN4rEyHdk93o7muCvtUEcg0AZo50ewFFn28WbEAH34_zZ4KnHuam9EVgF8ngAV_-4MFA0TUwxaeff1CSQxFhGC1ca4XhqlFPKWJlzwwkuccdLp-zOBz5pF9YFLQNQQ5HYg9XmZe73ESs17qD1BjzK-PoVek0GFg |
linkProvider | Taylor & Francis |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEB5BOVAOqC2ghrZ0D6g3F-_Lj2MFDQGSCvWhltNq1143SDSpauf_M2Ovo0Qq6oGr5fFjZmf2m9HsNwAfbZFYlVc6yp2mBAUxXGZLMoiXPtdedk00k7NkdKW-3-iblbMw1FZJOXTVEUW0sZqcm4rRfUvcJ0pjMtypqTMrP-ZJorVMn8MLnWE2gWs6_jVaRmMeBqygSEQy_Smefz1mbX9aYy9dx6CrvKLtXjTcgtcBRLKTzurb8MzPduDVZMnAWu_AJqHIjoT5DUxwMbDzUPZjFz-_nLKTwCXO8M3sq13UdJgS75mV8zs2pLa2ml3_bqYs8FKzi7s5GpUC41u4Gp5efh5FYY5CVGA-00Sae437uLdc-jRG06hEC3R17hOvMQMsuRLWc5-WSqTWWnTy0lnNC0SHlS4z-Q42ZvOZ3wXmEqVl5YTOXKqs0lmh4iKvYmnz1FcVH4DqtWeKQDJOsy7-GB64SHulG1K6CUofwPFS7L5j2XhKIF81jWna8kbVzSIx8gnZ_d6OJjhsbRDIIFLGcKcHcNTZdvklxMB9u7g3eOl2YWpvBKaBPB7A-JEbA0fT1BTTdgBOTQJJjHm0cKURjqdGOaWMlTk3nPASd7x0yr7_j186hJejy8nYjL-d_diDTUE1AeouEvuw0Tws_AECp8Z9aD3jL3nkCK8 |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BkVA58ChULBTwAXHLEr_yOFa0S4HuqqJUcLPsxOki2t1Vk1z49cwkzqqLQD30GmWi2DMef2N9_gbgrS0Sq_JKR7nTVKAghstsSQ7x0ufay55EM50lR2fq8w89sAnrQKukGrrqhSK6XE2Le1VWAyPuPVUxGW7URMzKxzxJtJbpXbiXIDwhVp-MZ-tkzEN_FTSJyGa4xPO_z2xsTxvipZsQ9LqsaLcVTR6BGwbRM1B-jdvGjYvff-k73mqUj-FhAKpsv4-sJ3DHL3bgwXSt8lrvwDYh1V7o-SlMMeDY13C0yE5PDg7ZftArZzg89tG2NV3YxHcW5fKSTYg6V7PvP5s5C9rX7PRyiYFDyfcZnE0Ov304ikKvhqjAmqmJNPcasYK3XPo0RverRAtMJ9wnXmOVWXIlrOc-LZVIrbWYSEpnNS8QgVa6zOQubC2WC_8cmEuUlpUTOnOpskpnhYqLvIqlzVNfVXwEanCRKYKQOfXTuDA86J0OU2do6kyYuhGM12arXsnjJoP8uv9N0x2hVH2_EyNvsN0bgsWEpFAbBEuIxjGl6hG86wNo_Sek8n3ergw-Om9N7Y3AUpPHIzj-x4tBB2puinnXZKcmgyTGWl240gjHU6OcUsbKnBtOmIw7XjplX9xiSG_g_snBxBx_mn15CduCjh2IwCT2YKu5av0rxGaNe92tvj_XOyda |
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=The+Rational+SPDE+Approach+for+Gaussian+Random+Fields+With+General+Smoothness&rft.jtitle=Journal+of+computational+and+graphical+statistics&rft.au=Bolin%2C+David&rft.au=Kirchner%2C+Kristin&rft.date=2020-04-02&rft.pub=Taylor+%26+Francis+Ltd&rft.issn=1061-8600&rft.eissn=1537-2715&rft.volume=29&rft.issue=2&rft.spage=274&rft.epage=285&rft_id=info:doi/10.1080%2F10618600.2019.1665537&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1061-8600&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1061-8600&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1061-8600&client=summon |