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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/65054


    Title: Bayesian semiparametric regression analysis of multicategorical time-space data
    Authors: Huang, Wen-tao;黃文濤(等)
    Contributors: 淡江大學經營決策學系
    Keywords: Categorical time-space data;forest damage;latent utility models;Markov random fields;MCMC;probit models;semiparametric Bayesian inference;unemployment
    Date: 2001-03
    Issue Date: 2011-10-20 16:31:10 (UTC+8)
    Abstract: We present a unified semiparametric Bayesian approach based on Markov random field 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 effects as well as nonlinear effects of metrical covariates in flexible semiparametric form. Trend and seasonal components, different types of covariates and spatial effects are all treated within the same general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference is fully Bayesian and uses MCMC techniques for posterior analysis. The approach in this paper 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.
    Relation: The annals of the institute of statistical mathemetics 53(1), pp.11-30
    DOI: 10.1023/A:1017904118167
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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