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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Bibliographic Details
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
Description
Summary: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.
ISSN:2227-7390