Model averaging for semiparametric varying coefficient quantile regression models

Missing data is a common problem in clinical data collection, which causes difficulty in the statistical analysis of such data. In this article, we consider the problem under a framework of a semiparametric partially linear model when observations are subject to complex missing pattern covariates. One natural question in the partially linear model is the choice of model structure, that is, how to decide which covariates are linear and which are nonlinear. If the correct model structure of the partially linear model is available, we propose to use a new imputation method called Partial Replacement IMputation Estimation (PRIME), which can overcome problems caused by incomplete data in the partially linear model. In the more challenging setting where the model structure is unknown a priori, we use PRIME in conjunction with model averaging (PRIME-MA) to adapt to the unknown model structure in the partially linear model. In simulation studies, we use various error distributions, sample sizes, missing data rates, covariate correlations, and noise levels, and PRIME outperforms other methods in almost all cases. With an unknown correct model structure, PRIME-MA has satisfactory performance in terms of prediction. Moreover, we conduct a study of influential factors in the Chinese Provincial Legal Funding Dataset from the Harvard Dataverse, which shows that our method performs better than the other models.

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Partial replacement imputation estimation for partially linear models with complex missing pattern covariates

Zhan

Zhang

2023

Stat Comput

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Model averaging for semiparametric varying coefficient quantile regression models

Cited by 1 publication

References 34 publications

Partial replacement imputation estimation for partially linear models with complex missing pattern covariates

Partial replacement imputation estimation for partially linear models with complex missing pattern covariates

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