Partial Likelihood Analysis of Categorical Time Series Models

Abstract

Partial likelihood analysis of two generalized logistic regression models for nominal and ordinal categorical time series is presented, taking into account stochastic time-dependent covariates. Under some conditions on the covariates, the resulting estimators are consistent and asymptotically normal. The analysis is applied to rainfall data where the goodness of fit is judged by a certain chi square statistic.