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

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  • A. N. Pettitt
  • T. T. Tran
  • M. A. Haynes
  • J. L. Hay

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. Copyright 2005 Royal Statistical Society.

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Bibliographic Info

Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society Series A.

Volume (Year): 169 (2006)
Issue (Month): 1 ()
Pages: 97-114

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Handle: RePEc:bla:jorssa:v:169:y:2006:i:1:p:97-114

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Cited by:
  1. Gretchen Carrigan & Adrian G. Barnett & Annette J. Dobson & Gita Mishra, . "Compensating for Missing Data from Longitudinal Studies Using WinBUGS," Journal of Statistical Software, American Statistical Association, vol. 19(i07).
  2. Irene Albarrán & J. Miguel Marín & Pablo J. Alonso, 2011. "Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view," Statistics and Econometrics Working Papers ws113729, Universidad Carlos III, Departamento de Estadística y Econometría.

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