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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Main Authors: Guozhi Hu, Weihu Cheng, Jie Zeng
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Mathematics
Subjects:
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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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
work_keys_str_mv AT guozhihu optimalmodelaveragingforsemiparametricpartiallylinearmodelswithcensoreddata
AT weihucheng optimalmodelaveragingforsemiparametricpartiallylinearmodelswithcensoreddata
AT jiezeng optimalmodelaveragingforsemiparametricpartiallylinearmodelswithcensoreddata