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Activity Number:
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303
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 14, 2002 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing*
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| Abstract - #300988 |
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Title:
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Empirical Sup Rejection Sampling
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Author(s):
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James Booth*+ and Brian Caffo and Anthony Davison
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Affiliation(s):
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University of Florida and Johns Hopkins University and Swiss Federal Institute of Technology
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Address:
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P.O. Box 118545, Gainesville, Florida, 32611-8545, USA
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Keywords:
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Candidate distribution ; Sample maximum ; Superefficient
estimator ; Weibull distribution
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Abstract:
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Rejection sampling thins out samples from a candidate density from which it is easy to simulate, to obtain samples from a more awkward target density. A prerequisite is knowledge of the finite supremum of the ratio of the target and candidate densities. This severely restricts application of the method because the supremum is often difficult to calculate. We use theoretical argument and numerical work to show that a practically perfect sample may be obtained by replacing the exact supremum with the maximum obtained from simulated candidates. We also provide diagnostics for failure of the method due to a bad choice of candidate distribution. The implication is that, essentially, no theoretical work is required to apply rejection sampling in practice if the objective is to approximate the mean of an i.i.d. sequence.
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