Abstract #301663

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JSM 2002 Abstract #301663
Activity Number: 140
Type: Topic Contributed
Date/Time: Monday, August 12, 2002 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing*
Abstract - #301663
Title: Self-Consistency and Wavelet Regressions with Irregular Designs
Author(s): Thomas Lee*+ and Xiao-Li Meng
Affiliation(s): Colorado State University and Harvard University
Address: , Fort Collins, Colorado, 80523-1877, US
Keywords: irregular design ; self-consistency ; wavelet regression
Abstract:

Self-consistency is a fundamental statistical principle for constructing the most efficient statistical estimators in many incomplete data problems. Our work applies this principle for wavelet regressions with irregular designs, by viewing an irregular design as an incomplete regular design. In this talk, we shall present our recent contributions beyond those that have been reported before. In particular, a much improved procedure, both in terms of computational speed and statistical efficiency, will be presented.

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