期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
卷 63, 期 -, 页码 339-355出版社
BLACKWELL PUBL LTD
DOI: 10.1111/1467-9868.00289
关键词
auto-normal model; lsing model; Markov chain Monte Carlo methods; off-line average; spatial models; stochastic approximation; very-soft-core model
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.
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