Bayesian analysis for zero-inflated regression models with the power prior: applications to road safety countermeasures.
We consider zero-inflated Poisson and zero-inflated negative binomial regression models to analyze discrete count data containing a considerable amount of zero observations. Analysis of current data could be empirically feasible if we utilize similar data based on previous studies. Ibrahim and Chen (2000) proposed the power prior to incorporate certain information from the historical data available from previous studies. The power prior is constructed by raising the likelihood function of the historical data to the power a(0), where 0< or =a(0)< or =1. The power prior is a useful informative prior in Bayesian inference. We estimate regression coefficients associated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. The empirical results show that the zero-inflated models with the power prior perform better than the frequentist approach.
Jang
Hakjin
H
Division of Applied Mathematics, Hanyang University, Ansan 426-791, South Korea.
Lee
Soobeom
S
Kim
Seong W
SW
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D006801:Humans; D012044:Regression Analysis