ESTIMATION IN EXPONENTIAL FAMILIES ON PERMUTATIONS

• 成果类型: Article
• 署名作者: Mukherjee, Sumit
• 署名单位: Columbia University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/15-AOS1389
• 发表日期: 2016
• 页码: 853-875
• 关键词: bivariate distributions; copulas; MODEL

摘要

Asymptotics of the normalizing constant are computed for a class of one parameter exponential families on permutations which include Mallows models with Spearmans's Footrule and Spearman's Rank Correlation Statistic. The MLE and a computable approximation of the MLE are shown to be consistent. The pseudo-likelihood estimator of Besag is shown to be root n-consistent. An iterative algorithm (IPFP) is proved to converge to the limiting normalizing constant. The Mallows model with Kendall's tau is also analyzed to demonstrate the flexibility of the tools of this paper.