Checking the adequacy of partial linear models with missing covariates at random

Wangli Xu, Xu Guo

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

In this paper, we consider the goodness-of-fit for checking whether the nonparametric function in a partial linear regression model with missing covariate at random is a parametric one or not. We estimate the selection probability by using parametric and nonparametric approaches. Two score type tests are constructed with the estimated selection probability. The asymptotic distributions of the test statistics are investigated under the null and local alterative hypothesis. Simulation studies are carried out to examine the finite sample performance of the sizes and powers of the tests. We apply the proposed procedure to a data set on the AIDS clinical trial group (ACTG 315) study.

Original languageEnglish (US)
Pages (from-to)473-490
Number of pages18
JournalAnnals of the Institute of Statistical Mathematics
Volume65
Issue number3
DOIs
StatePublished - Jun 1 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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