Model averaging procedure for varying-coefficient partially linear models with missing responses

Zeng, Jie; Cheng, Weihu; Hu, Guozhi; Rong, Yaohua

doi:10.1016/j.jkss.2018.04.004

Cited by 6 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the complexity of the missing data and the fact that the PLMs involve unknown nonparametric part, the literature about model averaging for PLMs with missing data is very scarce. Zeng et al (2018) proposed model averaging procedure for varying-coefficient PLMs with responses missing at random by using profile least-squares estimating procedure and inverse probability weighting method. However, their work requires that the model be correctly specified for the selection probability function.…”

mentioning

confidence: 99%

A robust model averaging approach for partially linear models with responses missing at random

Liang

Wang

2023

Scandinavian J Statistics

View full text Add to dashboard Cite

In this paper, with an assumed parametric model for the selection probability function, a robust model averaging estimation method is proposed for partially linear models with responses missing at random. The method is based on a weighted Mallows‐type criterion. The method is robust in the sense that the asymptotic optimality holds true as long as the true model of the selection probability function is some measurable function of its assumed model. The optimal weight vector for model averaging is obtained by minimizing the weighted Mallows‐type criterion. It is shown that the robust model averaging method achieves the lowest possible squared error asymptotically. Some simulation studies were conducted to evaluate the proposed method. An application to two real examples are provided as illustration.

show abstract

mentioning

confidence: 99%