Bayesian approach to errors-in-variables in count data regression models with departures from normality and overdispersion

Document Type

Article

Publication Date

1-1-2018

Abstract

In most practical applications, the quality of count data is often compromised due to errors-in-variables (EIVs). In this paper, we apply Bayesian approach to reduce bias in estimating the parameters of count data regression models that have mismeasured independent variables. Furthermore, the exposure model is misspecified with a flexible distribution, hence our approach remains robust against any departures from normality in its true underlying exposure distribution. The proposed method is also useful in realistic situations as the variance of EIVs is estimated instead of assumed as known, in contrast with other methods of correcting bias especially in count data EIVs regression models. We conduct simulation studies on synthetic data sets using Markov chain Monte Carlo simulation techniques to investigate the performance of our approach. Our findings show that the flexible Bayesian approach is able to estimate the values of the true regression parameters consistently and accurately.

Keywords

Count data regression, errors-in-variables, Bayesian, Markov chain Monte Carlo

Divisions

MathematicalSciences

Funders

Malaysian Ministry of Higher Education under the Fundamental Research Grant Scheme No: FP037-2014B

Publication Title

Journal of Statistical Computation and Simulation

Volume

88

Issue

2

Publisher

Taylor & Francis

This document is currently not available here.

Share

COinS