Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data
In the past few decades, model averaging has received extensive attention, and has been regarded as a feasible alternative to model selection. However, this work is mainly based on parametric model framework and complete dataset. This paper develops a frequentist model-averaging estimation for semip...
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MDPI AG
2023-02-01
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Online Access: | https://www.mdpi.com/2227-7390/11/3/734 |
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author | Guozhi Hu Weihu Cheng Jie Zeng |
author_facet | Guozhi Hu Weihu Cheng Jie Zeng |
author_sort | Guozhi Hu |
collection | DOAJ |
description | In the past few decades, model averaging has received extensive attention, and has been regarded as a feasible alternative to model selection. However, this work is mainly based on parametric model framework and complete dataset. This paper develops a frequentist model-averaging estimation for semiparametric partially linear models with censored responses. The nonparametric function is approximated by B-spline, and the weights in model-averaging estimator are picked up via minimizing a leave-one-out cross-validation criterion. The resulting model-averaging estimator is proved to be asymptotically optimal in the sense of achieving the lowest possible squared error. A simulation study demonstrates that the method in this paper is superior to traditional model-selection and model-averaging methods. Finally, as an illustration, the proposed procedure is further applied to analyze two real datasets. |
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institution | Directory Open Access Journal |
issn | 2227-7390 |
language | English |
last_indexed | 2024-03-11T09:34:56Z |
publishDate | 2023-02-01 |
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spelling | doaj.art-fa846300a2124a529de895d949f762112023-11-16T17:23:49ZengMDPI AGMathematics2227-73902023-02-0111373410.3390/math11030734Optimal Model Averaging for Semiparametric Partially Linear Models with Censored DataGuozhi Hu0Weihu Cheng1Jie Zeng2School of Mathematics and Statistics, Hefei Normal University, Hefei 230601, ChinaFaculty of Science, Beijing University of Technology, Beijing 100124, ChinaSchool of Mathematics and Statistics, Hefei Normal University, Hefei 230601, ChinaIn the past few decades, model averaging has received extensive attention, and has been regarded as a feasible alternative to model selection. However, this work is mainly based on parametric model framework and complete dataset. This paper develops a frequentist model-averaging estimation for semiparametric partially linear models with censored responses. The nonparametric function is approximated by B-spline, and the weights in model-averaging estimator are picked up via minimizing a leave-one-out cross-validation criterion. The resulting model-averaging estimator is proved to be asymptotically optimal in the sense of achieving the lowest possible squared error. A simulation study demonstrates that the method in this paper is superior to traditional model-selection and model-averaging methods. Finally, as an illustration, the proposed procedure is further applied to analyze two real datasets.https://www.mdpi.com/2227-7390/11/3/734model averagingasymptotic optimalityleave-one-out cross-validationpartially linear modelcensored data |
spellingShingle | Guozhi Hu Weihu Cheng Jie Zeng Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data Mathematics model averaging asymptotic optimality leave-one-out cross-validation partially linear model censored data |
title | Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data |
title_full | Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data |
title_fullStr | Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data |
title_full_unstemmed | Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data |
title_short | Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data |
title_sort | optimal model averaging for semiparametric partially linear models with censored data |
topic | model averaging asymptotic optimality leave-one-out cross-validation partially linear model censored data |
url | https://www.mdpi.com/2227-7390/11/3/734 |
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