Checking nonparametric component for partial linear regression model with missing response

Cuizhen Niu, Xu Guo, Wangli Xu, Lixing Zhu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The partial linear regression model is wildly used due to its well established theories, flexibility and easy interpretation. This paper aims to investigate the specification test of nonparametric component in partial linear model with response missing at random. Two quadratic conditional moment tests are proposed and both two test statistics own limiting normal distributions when nonparametric component is correctly specified. Our tests' virtue is that p-values can be easily determined based on limiting null distributions which are intractable for existing tests. The tests can detect the alternative hypotheses distinct from the null hypothesis at the optimal nonparametric rate for local smoothing-based methods. Simulation studies reveal that our tests can control type I error well and have excellent power performance. A HIV clinical trial real data is analyzed for illustrating our methods.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalJournal of Statistical Planning and Inference
Volume168
DOIs
StatePublished - Jan 1 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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