Consistent estimation of the accuracy of importance sampling using regenerative simulation

Sourabh Bhattacharya

Statistics & Probability Letters, 2008, vol. 78, issue 15, 2522-2527

Abstract: Importance sampling is a common technique traditionally used in cases where interest lies in estimation of characteristics of a density [pi](1), but samples are available from a different distribution [pi](0). It is important, however, to evaluate the accuracy of the estimate obtained using importance sampling. In cases where samples are obtained using Markov chain Monte Carlo methods, there does not seem to exist in the literature any consistent or easily computable estimate of the variance of the importance sampling estimator. In this paper we propose an estimator based on regenerative simulation that is consistent as well as easily computable.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(08)00159-4
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:78:y:2008:i:15:p:2522-2527

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().