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IMPROVING THE NORMALIZED IMPORTANCE SAMPLING ESTIMATOR

Published online by Cambridge University Press:  30 July 2012

Samim Ghamami  and
Sheldon M. Ross
Samim Ghamami
Affiliation:
Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089 E-mails: ghamami@usc.edu; smross@usc.edu
Sheldon M. Ross
Affiliation:
Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089 E-mails: ghamami@usc.edu; smross@usc.edu
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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.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 26 , Issue 4 , October 2012 , pp. 569 - 572
Copyright
Copyright © Cambridge University Press 2012

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References

1.Asmussen, S. & Glynn, P.W. (2007). Stochastic simulation. New York: Springer.Google Scholar
2.Asmussen, S., Kroese, D.P. & Rubinstein, R.Y. (2005). Heavy tails, importance sampling and cross-entropy. Stochastic Models 21(1): 57–76.CrossRefGoogle Scholar
3.Glasserman, P. (2004). Monte Carlo methods in financial engineering. Springer.Google Scholar
4.Liu, S.J. (2004). Monte Carlo strategies in scientific computing. Springer.CrossRefGoogle Scholar