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A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants
Author(s) -
Pettitt A. N.,
Tran T. T.,
Haynes M. A.,
Hay J. L.
Publication year - 2006
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2005.00389.x
Subject(s) - categorical variable , covariate , multinomial distribution , bayesian probability , econometrics , gibbs sampling , missing data , statistics , multilevel model , hierarchical database model , bayesian hierarchical modeling , data set , mathematics , computer science , bayesian inference , data mining
Summary. 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.