Improvements of the maximum pseudo-likelihood estimators in various spatial statistical models

作     者：Huang, FC Ogata, Y 

作者机构：Australian Natl Univ Ctr Math & Applicat Canberra ACT 0200 Australia Inst Stat Math Minato Ku Tokyo 106 Japan 

出 版 物：《JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS》 (计算与图解统计学杂志)

年 卷 期：1999年第8卷第3期

页      面：510-530页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：auto-normal model Gibbs sampling Ising model metropolis algorithm Newton-Raphson transformation pairwise interacted point process. 

摘      要：Maximum pseudo-likelihood estimation has hitherto been viewed as a practical but flawed alternative to maximum likelihood estimation, necessary because the maximum likelihood estimator is too hard to compute, but flawed because of its inefficiency when the spatial interactions are strong. We demonstrate that a single Newton-Raphson step starting from the maximum pseudo-likelihood estimator produces an estimator which is close to the maximum likelihood estimator in terms of its actual value, attained likelihood, and efficiency, even in the presence of strong interactions. This hybrid technique greatly increases the practical applicability of pseudo-likelihood-based estimation. Additionally, in the case of the spatial point processes, we propose a proper maximum pseudo-likelihood estimator which is different from the conventional one. The proper maximum pseudo-likelihood estimator clearly shows better performance than the conventional one does when the spatial interactions are strong.