Options
A generalized multiple-try version of the Reversible Jump algorithm
Date Issued
2014-04
Date Available
2017-02-22T12:20:53Z
Abstract
The Reversible Jump algorithm is one of the most widely used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. A generalized multiple-try version of this algorithm is proposed. The algorithm is based on drawing several proposals at each step and randomly choosing one of them on the basis of weights (selection probabilities) that may be arbitrarily chosen. Among the possible choices, a method is employed which is based on selection probabilities depending on a quadratic approximation of the posterior distribution. Moreover, the implementation of the proposed algorithm for challenging model selection problems, in which the quadratic approximation is not feasible, is considered. The resulting algorithm leads to a gain in efficiency with respect to the Reversible Jump algorithm, and also in terms of computational effort. The performance of this approach is illustrated for real examples involving a logistic regression model and a latent class model.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Italian Government
Type of Material
Journal Article
Publisher
Elsevier
Journal
Computational Statistics & Data Analysis
Volume
72
Start Page
298
End Page
314
Copyright (Published Version)
2013 Elsevier
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
10
Acquisition Date
Apr 17, 2024
Apr 17, 2024
Views
1619
Last Week
1
1
Last Month
1
1
Acquisition Date
Apr 17, 2024
Apr 17, 2024
Downloads
450
Last Week
3
3
Last Month
14
14
Acquisition Date
Apr 17, 2024
Apr 17, 2024