Bayesian truncated poisson regression with application to dutch illegal immigrant data

Borek D. Puza, Helen L. Johnson, Terence J. O'Neill, Simon C. Barry

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4 Citations (Scopus)
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Abstract

This article presents a Bayesian approach to the regression analysis of truncated data, with a focus on zero-truncated counts from the Poisson distribution. The approach provides inference not only on the regression coefficients but also on the total sample size and the parameters of the covariate distribution. The theory is applied to some illegal immigrant data from The Netherlands. Several models are fitted with the aid of Markov chain Monte Carlo methods and assessed via posterior predictive p-values. Inferences are compared with those obtained elsewhere using other approaches.

Original languageEnglish
Pages (from-to)1565-1577
Number of pages13
JournalCommunications in Statistics Part B: Simulation and Computation
Volume37
Issue number8
DOIs
Publication statusPublished - Sep 2008

Fingerprint

Poisson distribution
Poisson Regression
Regression analysis
Markov processes
Monte Carlo methods
Truncated Data
Markov Chain Monte Carlo Methods
p-Value
Regression Coefficient
Regression Analysis
Bayesian Approach
Covariates
Count
Sample Size
Zero
Model

Cite this

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Bayesian truncated poisson regression with application to dutch illegal immigrant data. / Puza, Borek D.; Johnson, Helen L.; O'Neill, Terence J.; Barry, Simon C.

In: Communications in Statistics Part B: Simulation and Computation, Vol. 37, No. 8, 09.2008, p. 1565-1577.

Research output: Contribution to journalArticleResearchpeer-review

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