JSM 2004 - Toronto

Abstract #301982

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Activity Number: 233
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 12:00 PM to 1:50 PM
Sponsor: Section on Statistical Computing
Abstract - #301982
Title: On the Parametric Bootstrap of an Asymptotic Variance
Author(s): Fassil Nebebe*+
Companies: Concordia University
Address: Dept. of Decision Sciences & MIS, Montreal, PQ, H3G 1M8, Canada
Keywords:
Abstract:

It is not uncommon that a sample statistic of observations from a parametric distribution has a normal limiting distribution but its variance does not exist for any finite sample size. Under this circumstance, the usual ideal bootstrap variance calculation does not lead to a converging or stable value (as the number of bootstrap sample increases). This paper addresses the issue of estimating numerically the variance of a limiting distribution when the variance does not exist for any finite sample sizes. This "asymptotic" variance is useful for interval estimation and also sample size determination. Contrary to the usual delta method which requires a case by case theoretical treatment, the present approach needs only the resampling of the parametric distribution under study.

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