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Bias-corrected Kullback–Leibler distance criterion based model selection with covariables missing at random

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  • Wei, Yuting
  • Wang, Qihua
  • Duan, Xiaogang
  • Qin, Jing

Abstract

A model selection problem for the conditional probability function of the response variable Y given the covariable vector (X,Z) is considered under the case where X is missing at random. And two novel model selection criteria are suggested. It is shown that the model selection by these two criteria is consistent and that the population parameter estimators, corresponding to the selected model, are also consistent and asymptotically normal. Extensive simulation studies are conducted to investigate the finite-sample performances of the proposed two criteria and a thorough comparison is made with some related model selection strategies. Moreover, two real data analyses are presented for illustrating the practical application of the proposed two criteria.

Suggested Citation

  • Wei, Yuting & Wang, Qihua & Duan, Xiaogang & Qin, Jing, 2021. "Bias-corrected Kullback–Leibler distance criterion based model selection with covariables missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:csdana:v:160:y:2021:i:c:s016794732100058x
    DOI: 10.1016/j.csda.2021.107224
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    1. Zhongqi Liang & Qihua Wang & Yuting Wei, 2022. "Robust model selection with covariables missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 539-557, June.

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