文章基本信息

标题：Comparison of the Sampling Efficiency in Spatial Autoregressive Model
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作者：Yoshihiro Ohtsuka 1 , Kazuhiko Kakamu
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2015
卷号：05
期号：01
页码：10-20
DOI：10.4236/ojs.2015.51002
语种：English
出版社：Scientific Research Publishing
摘要：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.
关键词：Acceptance-Rejection Metropolis-Hastings Algorithm; Griddy Gibbs Sampler; Markov Chain Monte Carlo (MCMC); Random Walk Metropolis-Hastings Algorithm; Spatial Autoregressive Model