Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation

• 成果类型: Article
• 署名作者: Gu, MG; Zhu, HT
• 署名单位: Chinese University of Hong Kong; University of Victoria
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/1467-9868.00289
• 发表日期: 2001
• 页码: 339-355
• 关键词: parameter-estimation; CONVERGENCE; Consistency; algorithm

摘要

We propose a two-stage algorithm for computing maximum likelihood estimates for a class of spatial models. The algorithm combines Markov chain Monte Carlo methods such as the Metropolis-Hastings-Green algorithm and the Gibbs sampler, and stochastic approximation methods such as the off-line average and adaptive search direction. A new criterion is built into the algorithm so stopping is automatic once the desired precision has been set. Simulation studies and applications to some real data sets have been conducted with three spatial models. We compared the algorithm proposed with a direct application of the classical Robbins-Monro algorithm using Wiebe's wheat data and found that our procedure is at least 15 times faster.