Statistical inference and visualization in scale-space using local likelihood

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SiZer (Significant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for exploratory data analysis with statistical inference. Various SiZer tools have been developed in the last decade, but most of them are not appropriate when the response variable takes discrete values. In this paper, we develop a SiZer for finding significant features using a local likelihood approach with local polynomial estimators. This tool improves the existing one (Li and Marron, 2005) by proposing a theoretically justified quantile in a confidence interval using advanced distribution theory. In addition, we investigate the asymptotic properties of the proposed tool. We conduct a numerical study to demonstrate the sample performance of SiZer using Bernoulli and Poisson models using simulated and real examples. (C) 2012 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2013-01
Language
English
Article Type
Article
Citation

COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.57, no.1, pp.336 - 348

ISSN
0167-9473
DOI
10.1016/j.csda.2012.06.023
URI
http://hdl.handle.net/10203/285777
Appears in Collection
MA-Journal Papers(저널논문)
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