Model checking for general linear regression with nonignorable missing response

Xu Guo, Lianlian Song, Yun Fang, Lixing Zhu

Research output: Contribution to journalArticle

Abstract

Model checking for the general linear regression model with nonignorable missing response is studied. Based on an exponential tilting model, two estimators are proposed for the unknown parameter in the regression model. Then, two empirical-process-based tests are constructed. The asymptotic properties of the proposed tests are investigated under the null and local alternative hypotheses in different scenarios. It is found that the two tests perform identically in the asymptotic sense. In addition, a nonparametric Monte Carlo test procedure is performed to obtain the critical values. Further, simulation studies are conducted to assess the performance of the proposed tests and compare them with other possible approaches. Finally, a real data set is analyzed for illustration.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalComputational Statistics and Data Analysis
Volume138
DOIs
StatePublished - Oct 2019

Fingerprint

Model checking
Linear regression
Model Checking
Exponential Tilting
Monte Carlo Test
Local Alternatives
Empirical Process
Non-parametric test
Linear Regression Model
Unknown Parameters
Asymptotic Properties
Null
Critical value
Regression Model
Simulation Study
Estimator
Scenarios
Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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Model checking for general linear regression with nonignorable missing response. / Guo, Xu; Song, Lianlian; Fang, Yun; Zhu, Lixing.

In: Computational Statistics and Data Analysis, Vol. 138, 10.2019, p. 1-12.

Research output: Contribution to journalArticle

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