Goodness-of-fitting for partial linear model with missing response at random

Wangli Xu, Xu Guo, Lixing Zhu

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

In this study, we consider the testing problem about the null hypothesis that the nonlinear part in the partial linear regression model with missing response at random is a parametric function or not against the alternative that it is nonparametric. By imputation and inverse probability weighting methods, we then construct two completed data sets. Two empirical process-based tests, from these completed data sets, are introduced. Under the null hypothesis and local alterative hypotheses, the limiting null distributions and power study of the test statistics are, respectively, investigated. A nonparametric Monte Carlo test procedure, which can automatically make the test procedure scale-invariant even when the test statistics are not scale-invariant, is applied to approximate the limiting null distributions of the test statistics. Simulation study is carried out to examine the performance of the tests. We illustrate the proposed method with a real data set on monozygotic twins.

Original languageEnglish (US)
Pages (from-to)103-118
Number of pages16
JournalJournal of Nonparametric Statistics
Volume24
Issue number1
DOIs
Publication statusPublished - Mar 1 2012

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this