期刊
MATHEMATICS AND COMPUTERS IN SIMULATION
卷 161, 期 -, 页码 32-51出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.matcom.2018.06.007
关键词
Markov chain Monte Carlo methods; Metropolis-Hastings algorithms; Model selection; Optimal scaling; Random walk Metropolis algorithms
类别
资金
- NSERC (Natural Sciences and Engineering Research Council of Canada)
- FRQNT (Le Fonds de recherche du Quebec - Nature et technologies)
- SOA (Society of Actuaries)
The reversible jump algorithm is a useful Markov chain Monte Carlo method introduced by Green (1995) that allows switches between subspaces of differing dimensionality, and therefore, model selection. Although this method is now increasingly used in key areas (e.g. biology and finance), it remains a challenge to implement it. In this paper, we focus on a simple sampling context in order to obtain theoretical results that lead to an optimal tuning procedure for the considered reversible jump algorithm, and consequently, to easy implementation. The key result is the weak convergence of the sequence of stochastic processes engendered by the algorithm. It represents the main contribution of this paper as it is, to our knowledge, the first weak convergence result for the reversible jump algorithm. The sampler updating the parameters according to a random walk, this result allows to retrieve the well-known 0.234 rule for finding the optimal scaling. It also leads to an answer to the question: with what probability should a parameter update be proposed comparatively to a model switch at each iteration? Crown Copyright (C) 2018 Published by Elsevier B.V. on behalf of International Association for Mathematics and Computers in Simulation (IMACS). All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据