TITLE:
Comparison of the Sampling Efficiency in Spatial Autoregressive Model
AUTHORS:
Yoshihiro Ohtsuka, Kazuhiko Kakamu
KEYWORDS:
Acceptance-Rejection Metropolis-Hastings Algorithm, Griddy Gibbs Sampler, Markov Chain Monte Carlo (MCMC), Random Walk Metropolis-Hastings Algorithm, Spatial Autoregressive Model
JOURNAL NAME:
Open Journal of Statistics,
Vol.5 No.1,
January
22,
2015
ABSTRACT: A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatial interaction in spatial autoregressive model from a Bayesian point of view. In addition, as an alternative approach, the griddy Gibbs sampler is proposed by [1] and utilized by [2]. This paper proposes an acceptance-rejection Metropolis-Hastings algorithm as a third approach, and compares these three algorithms through Monte Carlo experiments. The experimental results show that the griddy Gibbs sampler is the most efficient algorithm among the algorithms whether the number of observations is small or not in terms of the computation time and the inefficiency factors. Moreover, it seems to work well when the size of grid is 100.