Bayesian Semiparametric Regression Analysis of Multicategorical Time-space Data
نویسندگان
چکیده
SUMMARY We present a uniied semiparametric Bayesian approach based on Markov random eld priors for analyzing the dependence of multicategorical response variables on time, space and further covariates. The general model extends dynamic, or state space, models for categorical time series and longitudinal data by including spatial eeects as well as nonlinear eeects of metrical covariates in exible semiparametric form. Trend and seasonal components, diierent types of covariates and spatial eeects are all treated within the same general framework by assigning appropriate priors with diierent forms and degrees of smoothness. Inference is fully Bayesian and uses MCMC techniques for posterior analysis. We provide two approaches: The rst one is based on direct evaluation of observation likelihoods. The second one is based on latent semiparametric utility models and is particularly useful for probit models. The methods are illustrated by applications to unemployment data and a forest damage survey.
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تاریخ انتشار 2000