Goodness-of-fit tests for general linear models with covariates missed at random

Xu Guo, Wangli Xu

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


In this paper, we consider a model checking problem for general linear models with randomly missing covariates. Two types of score type tests with inverse probability weight, which is estimated by parameter and nonparameter methods respectively, are proposed to this goodness of fit problem. The asymptotic properties of the test statistics are developed under the null and local alternative hypothesis. Simulation study is carried out to present the performance of the sizes and powers of the tests. We illustrate the proposed method with a data set on monozygotic twins.

Original languageEnglish (US)
Pages (from-to)2047-2058
Number of pages12
JournalJournal of Statistical Planning and Inference
Issue number7
StatePublished - Jul 2012

All Science Journal Classification (ASJC) codes

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


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