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 language | English (US) |
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Pages (from-to) | 447-451 |
Number of pages | 5 |
Journal | Journal of the American Statistical Association |
Volume | 84 |
Issue number | 406 |
DOIs | |
State | Published - Jun 1989 |
Keywords
- Asymptotics
- Log-linear models
- Nonhomogeneous Markov chains
- Pseudolikelihood
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty