Bayesian and non-Bayesian analysis of gamma stochastic frontier models by Markov chain Monte Carlo methods

Hideo Kozumi, Xingyuan Zhang

Research output: Contribution to journalArticle

3 Citations (Scopus)


This paper considers simulation-based approaches for the gamma stochastic frontier model. Efficient Markov chain Monte Carlo methods are proposed for sampling the posterior distribution of the parameters. Maximum likelihood estimation is also discussed based on the stochastic approximation algorithm. The methods are applied to a data set of the U.S. electric utility industry.

Original languageEnglish
Pages (from-to)575-593
Number of pages19
JournalComputational Statistics
Issue number4
Publication statusPublished - 2006



  • Acceptance-rejection Metropolis-Hastings algorithm
  • Auxiliary variable method
  • Marginal likelihood
  • Markov chain Monte Carlo
  • Stochastic approximation
  • Stochastic frontier model

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Statistics, Probability and Uncertainty

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