Bayesian analysis of the Logit model and comparison of two Metropolis-Hastings strategies

  • Authors:

  • Anas Altaleb;Didier Chauveau

  • Affiliations:

  • Department of Mathematics and Statistics, Faculty of Engineering, University of Damascus, Damascus, Syria;Equipe d' Analyse et Mathématiques Appliquées, Université de Marne-la-Vallée, 5 Bd. Descartes, Champs sur Marne, 77454 Marne la Vallee, Cedex 2, France

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2002

Quantified Score

Hi-index 0.03

Visualization

Abstract

We examine some Markov chain Monte Carlo (MCMC) methods for a generalized non-linear regression model, the Logit model. It is first shown that MCMC algorithms may be used since the posterior is proper under the choice of non-informative priors. Then two non-standard MCMC methods are compared: a Metropolis-Hastings algorithm with a bivariate normal proposal resulting from an approximation, and a Metropolis-Hastings algorithm with an adaptive proposal. The results presented here are illustrated by simulations, and show the good behavior of both methods, and superior performances of the method with an adaptive proposal in terms of convergence to the stationary distribution and exploration of the posterior distribution surface.