Bayesian analysis for zero-inflated regression models with the power prior: Applications to road safety countermeasures

Hakjin Jang, Soobeom Lee, Seong W. Kim

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

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 a0, where 0 ≤ a0 ≤ 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. Crown

Original languageEnglish
Pages (from-to)540-547
Number of pages8
JournalAccident Analysis and Prevention
Volume42
Issue number2
DOIs
StatePublished - Mar 2010

Keywords

  • Accident prediction model
  • Historical data
  • Metropolis-Hastings algorithm
  • Power prior
  • Zero-inflated regression model

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