A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants

Pettitt, A. N., Tran, T. T., Haynes, M. A. and Hay, J. L. (2006) A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants. Journal of The Royal Statistical Society Series A-Statistics In Society, 169 1: 97-114. doi:10.1111/j.1467-985X.2005.00389.x


Author Pettitt, A. N.
Tran, T. T.
Haynes, M. A.
Hay, J. L.
Title A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants
Journal name Journal of The Royal Statistical Society Series A-Statistics In Society   Check publisher's open access policy
ISSN 0964-1988
Publication date 2006-10
Sub-type Article (original research)
DOI 10.1111/j.1467-985X.2005.00389.x
Volume 169
Issue 1
Start page 97
End page 114
Total pages 18
Place of publication Oxford
Publisher Blackwell
Collection year 2006
Language eng
Subject C1
230204 Applied Statistics
780101 Mathematical sciences
CX
Abstract The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.
Keyword Bayesian hierarchical models
Generalized linear mixed models
Longitudinal data analysis
Missing data
Random effects
Q-Index Code C1

 
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