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June 2008 Discussion of: Treelets—An adaptive multi-scale basis for sparse unordered data
Xing Qiu
Ann. Appl. Stat. 2(2): 484-488 (June 2008). DOI: 10.1214/08-AOAS137E

Abstract

This is a discussion of paper “Treelets—An adaptive multi-scale basis for sparse unordered data” by Ann B. Lee, Boaz Nadler and Larry Wasserman. In this paper the authors defined a new type of dimension reduction algorithm, namely, the treelet algorithm. The treelet method has the merit of being completely data driven, and its decomposition is easier to interpret as compared to PCR. It is suitable in some certain situations, but it also has its own limitations. I will discuss both the strength and the weakness of this method when applied to microarray data analysis.

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Xing Qiu. "Discussion of: Treelets—An adaptive multi-scale basis for sparse unordered data." Ann. Appl. Stat. 2 (2) 484 - 488, June 2008. https://doi.org/10.1214/08-AOAS137E

Information

Published: June 2008
First available in Project Euclid: 3 July 2008

zbMATH: 05591283
MathSciNet: MR2524341
Digital Object Identifier: 10.1214/08-AOAS137E

Keywords: Dependence , microarray , Treelet

Rights: Copyright © 2008 Institute of Mathematical Statistics

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Vol.2 • No. 2 • June 2008
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