Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 19, Issue 3, Pages 497-513Publisher
TAYLOR & FRANCIS INC
DOI: 10.1198/jcgs.2010.09120
Keywords
Effective sample size; Multiple slope testing; Nonparametric quantile regression; Robust variance estimation; Running regression quantile; SiZer
Categories
Funding
- National Science Foundation [0504737, 0707037]
- Hong Kong Research Grants Council under CERG [401507]
- Chinese University of Hong Kong
- National Security Agency [H982300810056]
- University of Georgia Research Computing Center
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0504737] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0707037] Funding Source: National Science Foundation
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The SiZer methodology proposed by Chaudhuri and Marron (1999) is a valuable tool for conducting exploratory data analysis. Since its inception different versions of SiZer have been proposed in the literature. Most of these SiZer variants are targeting the mean structure of the data, and are incapable of providing any information about the quantile composition of the data. To till this need, this article proposes a quantile version of SiZer for the regression setting. By inspecting the SiZer maps produced by this new SiZer, real quantile structures hidden in a dataset can be more effectively revealed, while at the same time spurious features can be filtered out. The utility of this quantile SiZer is illustrated via applications to both real data and simulated examples. This article has supplementary material online.
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