Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation |
| |
Authors: | Ming Gao Gu & Hong-Tu Zhu |
| |
Institution: | Chinese University of Hong Kong, People's Republic of China,;University of Victoria, Canada |
| |
Abstract: | 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. |
| |
Keywords: | Auto-normal model Ising model Markov chain Monte Carlo methods Off-line average Spatial models Stochastic approximation Very-soft-core model |
|