Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data

Guozhi Hu, Weihu Cheng and Jie Zeng ()
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Guozhi Hu: School of Mathematics and Statistics, Hefei Normal University, Hefei 230601, China
Weihu Cheng: Faculty of Science, Beijing University of Technology, Beijing 100124, China
Jie Zeng: School of Mathematics and Statistics, Hefei Normal University, Hefei 230601, China

Mathematics, 2023, vol. 11, issue 3, 1-21

Abstract: 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.

Keywords: model averaging; asymptotic optimality; leave-one-out cross-validation; partially linear model; censored data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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