Skip to main content
Article
A More Efficient Gibbs Sampler Estimation Using Steady States Simulation: Application to Public Health Studies
Statistical Computation and Simulation (2014)
  • Martin Dunbar, Georgia Southern University
  • Hani Samawi, Georgia Southern University
  • Robert Vogel, Georgia Southern University
  • Lili Yu, Georgia Southern University
Abstract

Markov chain Monte Carlo methods, in particular, the Gibbs sampler, are widely used algorithms both in application and theoretical works in the classical and Bayesian paradigms. However, these algorithms are often computer intensive. Samawi et al. [Steady-state ranked Gibbs sampler. J. Stat. Comput. Simul. 2012;82(8), 1223–1238. doi:10.1080/00949655.2011.575378] demonstrate through theory and simulation that the dependent steady-state Gibbs sampler is more efficient and accurate in model parameter estimation than the original Gibbs sampler. This paper proposes the independent steady-state Gibbs sampler (ISSGS) approach to improve the original Gibbs sampler in multidimensional problems. It is demonstrated that ISSGS provides accuracy with unbiased estimation and improves the performance and convergence of the Gibbs sampler in multidimensional problems.

Keywords
  • Markov chain Monte Carlo (MCMC) methods,
  • Gibbs sampler,
  • dependent steady-state Gibbs sampler (DSSGS),
  • ranked set sampling,
  • independent steady-state Gibbs sampler (ISSGS),
  • steady-state ranked simulated sampling
Disciplines
Publication Date
2014
Citation Information
Martin Dunbar, Hani Samawi, Robert Vogel and Lili Yu. "A More Efficient Gibbs Sampler Estimation Using Steady States Simulation: Application to Public Health Studies" Statistical Computation and Simulation Vol. 84 Iss. 9 (2014)
Available at: http://works.bepress.com/hani_samawi/9/