Mean response estimation with missing response in the presence of high-dimensional covariates
This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model sp...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Yongjin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Rechteinformationen: |
Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Communications in statistics / Theory and methods - London : Taylor and Francis, 1982, 46(2017), 2, Seite 628-643 |
|---|---|
| Übergeordnetes Werk: |
volume:46 ; year:2017 ; number:2 ; pages:628-643 |
| Links: |
|---|
| DOI / URN: |
10.1080/03610926.2014.1002935 |
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| 520 | |a This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. | ||
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10.1080/03610926.2014.1002935 doi PQ20170301 (DE-627)OLC1988491088 (DE-599)GBVOLC1988491088 (PRQ)c1900-bfc70faef40e0d4a4342f00f7c79e46de7725e3400742a3809907b30f1da1ca30 (KEY)0108848320170000046000200628meanresponseestimationwithmissingresponseinthepres DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Li, Yongjin verfasserin aut Mean response estimation with missing response in the presence of high-dimensional covariates 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 62J02 Missing response Weighted-bandwidth Central mean subspace 62G05 Imputation 62H12 Kernel regression Economic models Methods Wang, Qihua oth Zhu, Liping oth Ding, Xiaobo oth Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 46(2017), 2, Seite 628-643 (DE-627)129862290 (DE-600)283673-7 (DE-576)015173747 0361-0926 nnns volume:46 year:2017 number:2 pages:628-643 http://dx.doi.org/10.1080/03610926.2014.1002935 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610926.2014.1002935 http://search.proquest.com/docview/1828678809 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 2 628-643 |
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10.1080/03610926.2014.1002935 doi PQ20170301 (DE-627)OLC1988491088 (DE-599)GBVOLC1988491088 (PRQ)c1900-bfc70faef40e0d4a4342f00f7c79e46de7725e3400742a3809907b30f1da1ca30 (KEY)0108848320170000046000200628meanresponseestimationwithmissingresponseinthepres DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Li, Yongjin verfasserin aut Mean response estimation with missing response in the presence of high-dimensional covariates 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 62J02 Missing response Weighted-bandwidth Central mean subspace 62G05 Imputation 62H12 Kernel regression Economic models Methods Wang, Qihua oth Zhu, Liping oth Ding, Xiaobo oth Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 46(2017), 2, Seite 628-643 (DE-627)129862290 (DE-600)283673-7 (DE-576)015173747 0361-0926 nnns volume:46 year:2017 number:2 pages:628-643 http://dx.doi.org/10.1080/03610926.2014.1002935 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610926.2014.1002935 http://search.proquest.com/docview/1828678809 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 2 628-643 |
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10.1080/03610926.2014.1002935 doi PQ20170301 (DE-627)OLC1988491088 (DE-599)GBVOLC1988491088 (PRQ)c1900-bfc70faef40e0d4a4342f00f7c79e46de7725e3400742a3809907b30f1da1ca30 (KEY)0108848320170000046000200628meanresponseestimationwithmissingresponseinthepres DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Li, Yongjin verfasserin aut Mean response estimation with missing response in the presence of high-dimensional covariates 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 62J02 Missing response Weighted-bandwidth Central mean subspace 62G05 Imputation 62H12 Kernel regression Economic models Methods Wang, Qihua oth Zhu, Liping oth Ding, Xiaobo oth Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 46(2017), 2, Seite 628-643 (DE-627)129862290 (DE-600)283673-7 (DE-576)015173747 0361-0926 nnns volume:46 year:2017 number:2 pages:628-643 http://dx.doi.org/10.1080/03610926.2014.1002935 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610926.2014.1002935 http://search.proquest.com/docview/1828678809 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 2 628-643 |
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10.1080/03610926.2014.1002935 doi PQ20170301 (DE-627)OLC1988491088 (DE-599)GBVOLC1988491088 (PRQ)c1900-bfc70faef40e0d4a4342f00f7c79e46de7725e3400742a3809907b30f1da1ca30 (KEY)0108848320170000046000200628meanresponseestimationwithmissingresponseinthepres DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Li, Yongjin verfasserin aut Mean response estimation with missing response in the presence of high-dimensional covariates 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 62J02 Missing response Weighted-bandwidth Central mean subspace 62G05 Imputation 62H12 Kernel regression Economic models Methods Wang, Qihua oth Zhu, Liping oth Ding, Xiaobo oth Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 46(2017), 2, Seite 628-643 (DE-627)129862290 (DE-600)283673-7 (DE-576)015173747 0361-0926 nnns volume:46 year:2017 number:2 pages:628-643 http://dx.doi.org/10.1080/03610926.2014.1002935 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610926.2014.1002935 http://search.proquest.com/docview/1828678809 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 2 628-643 |
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10.1080/03610926.2014.1002935 doi PQ20170301 (DE-627)OLC1988491088 (DE-599)GBVOLC1988491088 (PRQ)c1900-bfc70faef40e0d4a4342f00f7c79e46de7725e3400742a3809907b30f1da1ca30 (KEY)0108848320170000046000200628meanresponseestimationwithmissingresponseinthepres DE-627 ger DE-627 rakwb eng 510 DE-600 31.73 bkl Li, Yongjin verfasserin aut Mean response estimation with missing response in the presence of high-dimensional covariates 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. Nutzungsrecht: © 2017 Taylor & Francis Group, LLC 2017 62J02 Missing response Weighted-bandwidth Central mean subspace 62G05 Imputation 62H12 Kernel regression Economic models Methods Wang, Qihua oth Zhu, Liping oth Ding, Xiaobo oth Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 46(2017), 2, Seite 628-643 (DE-627)129862290 (DE-600)283673-7 (DE-576)015173747 0361-0926 nnns volume:46 year:2017 number:2 pages:628-643 http://dx.doi.org/10.1080/03610926.2014.1002935 Volltext http://www.tandfonline.com/doi/abs/10.1080/03610926.2014.1002935 http://search.proquest.com/docview/1828678809 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ AR 46 2017 2 628-643 |
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Mean response estimation with missing response in the presence of high-dimensional covariates |
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| title_full |
Mean response estimation with missing response in the presence of high-dimensional covariates |
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Li, Yongjin |
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Communications in statistics / Theory and methods |
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Communications in statistics / Theory and methods |
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eng |
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10.1080/03610926.2014.1002935 |
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| title_sort |
mean response estimation with missing response in the presence of high-dimensional covariates |
| title_auth |
Mean response estimation with missing response in the presence of high-dimensional covariates |
| abstract |
This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. |
| abstractGer |
This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. |
| abstract_unstemmed |
This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations. |
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| title_short |
Mean response estimation with missing response in the presence of high-dimensional covariates |
| url |
http://dx.doi.org/10.1080/03610926.2014.1002935 http://www.tandfonline.com/doi/abs/10.1080/03610926.2014.1002935 http://search.proquest.com/docview/1828678809 |
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Wang, Qihua Zhu, Liping Ding, Xiaobo |
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Wang, Qihua Zhu, Liping Ding, Xiaobo |
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10.1080/03610926.2014.1002935 |
| up_date |
2024-07-03T18:04:09.043Z |
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