Estimation in semiparametric models with missing data
This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputati...
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| Published in | Annals of the Institute of Statistical Mathematics Vol. 65; no. 4; pp. 785 - 805 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
Springer Japan
01.08.2013
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputation method for the missing values, which then leads to imputed estimating equations for the finite dimensional parameter of interest. The asymptotic normality of the parameter estimator is proved in a general setting, and is investigated in detail for a number of specific semiparametric models. Finally, we study the small sample performance of the proposed estimator via simulations. |
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| AbstractList | This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputation method for the missing values, which then leads to imputed estimating equations for the finite dimensional parameter of interest. The asymptotic normality of the parameter estimator is proved in a general setting, and is investigated in detail for a number of specific semiparametric models. Finally, we study the small sample performance of the proposed estimator via simulations. [PUBLICATION ABSTRACT] This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputation method for the missing values, which then leads to imputed estimating equations for the finite dimensional parameter of interest. The asymptotic normality of the parameter estimator is proved in a general setting, and is investigated in detail for a number of specific semiparametric models. Finally, we study the small sample performance of the proposed estimator via simulations. |
| Author | Van Keilegom, Ingrid Chen, Song Xi |
| Author_xml | – sequence: 1 givenname: Song Xi surname: Chen fullname: Chen, Song Xi organization: Guanghua School of Management and Center for Statistical Science, Peking University, Iowa State University – sequence: 2 givenname: Ingrid surname: Van Keilegom fullname: Van Keilegom, Ingrid email: ingrid.vankeilegom@uclouvain.be organization: Institute of Statistics, Université catholique de Louvain |
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| CitedBy_id | crossref_primary_10_1080_10485252_2019_1702660 crossref_primary_10_1007_s00362_016_0784_5 crossref_primary_10_1007_s42952_024_00279_y crossref_primary_10_3390_stats7030056 crossref_primary_10_1080_24754269_2019_1672021 crossref_primary_10_1007_s11749_018_0591_5 crossref_primary_10_1109_ACCESS_2020_2970741 crossref_primary_10_1080_03610918_2021_1873371 crossref_primary_10_1093_biomet_asx025 |
| Cites_doi | 10.2307/1912705 10.1093/biomet/63.3.581 10.1016/0304-4076(93)90114-K 10.1214/08-AOS642 10.1016/j.jmva.2007.05.004 10.1007/s10463-007-0137-1 10.1214/07-AOS585 10.1214/009053607000000947 10.1016/j.jmva.2006.10.003 10.1002/9781119013563 10.1111/1468-0262.00461 10.1007/978-1-4899-3244-0 10.1080/01621459.1994.10476818 10.1214/aos/1024691087 10.1198/016214507000001058 10.1198/016214504000000449 10.1016/j.jspi.2009.11.017 10.1007/978-1-4757-2545-2 10.2307/1913713 10.1214/aos/1176349020 10.1111/1468-0262.00470 |
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| Copyright | The Institute of Statistical Mathematics, Tokyo 2012 The Institute of Statistical Mathematics, Tokyo 2013 |
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| DOI | 10.1007/s10463-012-0393-6 |
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| Keywords | Copulas Single-index model Missing at random Nuisance function Imputation Partially linear model Kernel smoothing Semiparametric model |
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| SubjectTerms | Asymptotic properties Computer simulation Economics Estimating Estimators Finance Generalized linear models Insurance Management Mathematical analysis Mathematical models Mathematics Mathematics and Statistics Missing data Parameter estimation Samples Statistical analysis Statistics Statistics for Business Studies |
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