Estimates and standard errors for ratios of normalizing constants from multiple Markov chains via regeneration

• 成果类型: Article
• 署名作者: Doss, Hani; Tan, Aixin
• 署名单位: State University System of Florida; University of Florida; University of Iowa
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12049
• 发表日期: 2014
• 页码: 683-712
• 关键词: monte-carlo integration; empirical distributions; models; CONVERGENCE; algorithms; rates

摘要

In the classical biased sampling problem, we have k densities pi(1)(.), ... , pi(k)(.), each known up to a normalizing constant, i.e., for l = 1, ... , k, pi(l)(.) = v(l)(.)/m(l), where v(l)(.)is a known function and m(l) is an unknown constant. For each l, we have an independent and identically distributed sample from pi(l), and the problem is to estimate the ratios m(l)/m(s) for all l and all s. This problem arises frequently in several situations in both frequentist and Bayesian inference. An estimate of the ratios was developed and studied by Vardi and his co-workers over two decades ago, and there has been much subsequent work on this problem from many perspectives. In spite of this, there are no rigorous results in the literature on how to estimate the standard error of the estimate. We present a class of estimates of the ratios of normalizing constants that are appropriate for the case where the samples from the pi(l)s are not necessarily independent and identically distributed sequences but are Markov chains. We also develop an approach based on regenerative simulation for obtaining standard errors for the estimates of ratios of normalizing constants. These standard error estimates are valid for both the independent and identically distributed samples case and the Markov chain case.