JSM 2005 Online Program

Abstract #303063

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	74
Type:	Contributed
Date/Time:	Sunday, August 7, 2005 : 8:00 PM to 9:50 PM
Sponsor:	Biometrics Section
Abstract - #303063
Title:	Undercoverage of Wavelet-based Resampling Confidence Intervals
Author(s):	Liansheng Tang*+ and William R. Schucany and Wayne Woodward
Companies:	Southern Methodist University and Southern Methodist University and Southern Methodist University
Address:	5657 Amesbury Dr Apt 2008, Dallas, TX, 75206, United States
Keywords:	wavelet ; wavestrap ; discrete wavelet transform ; inverse discrete wavelet transform ; bootstrap
Abstract:	The decorrelating of discrete wavelet transformation seems a nice property to avoid estimating the correlation structure in original data space. People have used wavestrap methods in recent years without theoretical or empirical proof of its validity. However, our simulation studies show the wavestraps give undercoverage of the statistic of interest related to mean structure of data as well as a simple linear regression coefficient. It is disappointing that the wavestrap is not able to give valid resamples for white noise sequence, which further implies the invalidity of the wavestraps on dependent data. Thus, the wavestrap method is neither preferred in obtaining resamples related to mean structure nor in the linear regression analysis and should be used with careful consideration. Other resampling methods such as spatial bootstrap and block bootstrap should be applied on spatial-correlated data instead of the wavetstrap. The reasons for the undercoverages of the wavestrap also are discussed in this paper.

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