Skip to main content

A Bootstrap Likelihood approach to Bayesian Computation

Leisen, Fabrizio, Marin, Juan Miguel, Zhu, Weixuan (2016) A Bootstrap Likelihood approach to Bayesian Computation. Australian and New Zealand Journal of Statistics, 58 (2). pp. 227-244. ISSN 1369-1473. E-ISSN 1467-842X. (doi:10.1111/anzs.12156) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1111/anzs.12156

Abstract

There is an increasing amount of literature focused on Bayesian computational

methods to address problems with intractable likelihood. One approach is a set of

algorithms known as Approximate Bayesian Computational (ABC) methods. One

of the problems of these algorithms is that the performance depends on the tuning

of some parameters, such as the summary statistics, distance and tolerance level. To

bypass this problem, Mengersen, Pudlo and Robert (2013) introduced an alterna-

tive method based on empirical likelihood, which can be easily implemented when

a set of constraints, related to the moments of the distribution, is known. However,

the choice of the constraints is sometimes challenging. To overcome this problem,

we propose an alternative method based on a bootstrap likelihood approach. The

method is easy to implement and in some cases it is faster than the other approaches.

The performance of the algorithm is illustrated with examples in Population Genetics, Time Series and Stochastic Differential Equations. Finally, we test the method

on a real dataset.

Item Type: Article
DOI/Identification number: 10.1111/anzs.12156
Subjects: H Social Sciences > HA Statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Fabrizio Leisen
Date Deposited: 21 Oct 2015 07:30 UTC
Last Modified: 01 Aug 2019 10:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/51108 (The current URI for this page, for reference purposes)
Leisen, Fabrizio: https://orcid.org/0000-0002-2460-6176
  • Depositors only (login required):