Improving the Normalized Importance Sampling Estimator

Probability in the Engineering and Informational Sciences, 2012

Posted: 27 Oct 2013

See all articles by Samim Ghamami

Samim Ghamami

Securities and Exchange Commission (SEC); New York University (NYU); University of California, Berkeley - Center for Risk Management Research

Sheldon Ross

University of Southern California - Viterbi School of Engineering

Date Written: October 20, 2012

Abstract

The normalized importance sampling estimator allows the target density f to be known only up to a multiplicative constant. We indicate how it can be derived by a delta method-based approximation of a Rao-Blackwellized acceptance rejection estimator. Using additional terms in the delta method then results on a new estimator that also only requires f to be known only up to a multiplicative constant. Numerical examples indicate that the new estimator usually outperforms the normalized importance sampling estimator in terms of mean square error.

Keywords: Monte Carlo simulation, importance sampling, Delta method

JEL Classification: C15

Suggested Citation

Ghamami, Samim and Ross, Sheldon, Improving the Normalized Importance Sampling Estimator (October 20, 2012). Probability in the Engineering and Informational Sciences, 2012, Available at SSRN: https://ssrn.com/abstract=2345765

Samim Ghamami (Contact Author)

Securities and Exchange Commission (SEC) ( email )

450 Fifth Street, NW
Washington, DC 20549-1105
United States

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
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University of California, Berkeley - Center for Risk Management Research ( email )

581 Evans Hall
Berkely, CA 94720
United States

Sheldon Ross

University of Southern California - Viterbi School of Engineering ( email )

3650 McClintock Ave
Los Angeles, CA
United States

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