A class of logistic regression models for multivariate binary time series

Kung Yee Liang, Scott L. Zeger

Research output: Contribution to journalArticlepeer-review

Abstract

A logistic model for multivariate binary time series is proposed. First, we establish the equivalence between a log-linear model for the marginal distribution of a multivariate binary random vector and logistic models for the conditional distributions of each component given the others. The logistic formulation is used to describe a Markov chain for each series, which implies a Markov model for the vector of time series. A pseudolikelihood estimation procedure is presented. The methods are illustrated with data on psychosomatic and psychological diagnoses for families in a health-maintenance plan.

Original languageEnglish (US)
Pages (from-to)447-451
Number of pages5
JournalJournal of the American Statistical Association
Volume84
Issue number406
DOIs
StatePublished - Jun 1989

Keywords

  • Asymptotics
  • Log-linear models
  • Nonhomogeneous Markov chains
  • Pseudolikelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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