Bayesian semiparametric regression analysis of multicategorical time-space data
學年 89
學期 2
出版（發表）日期 2001-03-01
作品名稱 Bayesian semiparametric regression analysis of multicategorical time-space data
作品名稱（其他語言）
著者 Huang, Wen-tao; 黃文濤(等)
單位 淡江大學經營決策學系
出版者
著錄名稱、卷期、頁數 The annals of the institute of statistical mathemetics 53(1), pp.11-30
摘要 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.
關鍵字 Categorical time-space data;forest damage;latent utility models;Markov random fields;MCMC;probit models;semiparametric Bayesian inference;unemployment
語言 en
ISSN 0020-3157 1572-9052
期刊性質 國內
收錄於
產學合作
通訊作者
審稿制度 否
國別 DEU
公開徵稿
出版型式 ,電子版,紙本
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