Logistic regression with outcome and covariates missing separately or simultaneously

Shu Hui Hsieh, Chin-Shang Li, Shen Ming Lee

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Estimation methods are proposed for fitting logistic regression in which outcome and covariate variables are missing separately or simultaneously. One of the two proposed estimators isan extension of the validation likelihood estimator of Breslow and Cain (1988). The other is a joint conditional likelihood estimator that uses both validation and non-validation data. Large sample properties of the proposed estimators are studied under certain regularity conditions. Simulation results show that the joint conditional likelihood estimator is more efficient than the validation likelihood estimator, weighted estimator, and complete-case estimator. The practical use of the proposed methods is illustrated with data from a cable TV survey study in Taiwan.

Original languageEnglish (US)
Pages (from-to)32-54
Number of pages23
JournalComputational Statistics and Data Analysis
Volume66
DOIs
StatePublished - 2013

Keywords

  • Covariate missing
  • Joint conditional likelihood
  • Outcome missing
  • Validation likelihood

ASJC Scopus subject areas

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

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