Bayesian semiparametric regression analysis of multicategorical time-space data

機構典藏 > College of Business and Management > Department of Management Sciences > Journal Article > Item 987654321/65054

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/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:	[Department of Management Sciences] Journal Article

Files in This Item:

File	Description	Size	Format
Bayesian semiparametric regression analysis of multicategorical time-space data.pdf		1278Kb	Adobe PDF	24	View/Open
index.html		0Kb	HTML	121	View/Open