Bayesian variable selection for Poisson regression with underreported responses

Authors:
Stephanie Powers;Richard Gerlach;James Stamey
Affiliations:
Red Deer College, Red Deer, Alberta, Canada;University of Sydney, NSW, Australia;Baylor University, Waco, TX, USA
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 3
Cited 1

Determining sample size to evaluate and compare the accuracy of binary diagnostic tests in the presence of partial disease verification

Computational Statistics & Data Analysis
Spatial-temporal association between fine particulate matter and daily mortality

Computational Statistics & Data Analysis
Parameter estimation in a model for misclassified Markov data - a Bayesian approach

Computational Statistics & Data Analysis

Editorial: Special issue on variable selection and robust procedures

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Variable selection for Poisson regression when the response variable is potentially underreported is considered. A logistic regression model is used to model the latent underreporting probabilities. An efficient MCMC sampling scheme is designed, incorporating uncertainty about which explanatory variables affect the dependent variable and which affect the underreporting probabilities. Validation data is required in order to identify and estimate all parameters. A simulation study illustrates favorable results both in terms of variable selection and parameter estimation. Finally, the procedure is applied to a real data example concerning deaths from cervical cancer.