Nonparametric Comparison of Multiple Regression Curves in Scale-Space

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This article concerns testing the equality of multiple curves in a nonparametric regression context. The proposed test forms an ANOVA type test statistic based on kernel smoothing and examines the ratio of between-and within-group variations. The empirical distribution of the test statistic is derived using a permutation test. Unlike traditional kernel smoothing approaches, the test is conducted in scale-space so that it does not require the selection of an optimal smoothing level, but instead considers a wide range of scales. The proposed method also visualizes its testing results as a color map and graphically summarizes the statistical differences between curves across multiple locations and scales. A numerical study using simulated and real examples is conducted to demonstrate the finite sample performance of the proposed method.
Publisher
AMER STATISTICAL ASSOC
Issue Date
2014-09
Language
English
Article Type
Article
Citation

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, v.23, no.3, pp.657 - 677

ISSN
1061-8600
DOI
10.1080/10618600.2013.822816
URI
http://hdl.handle.net/10203/285756
Appears in Collection
MA-Journal Papers(저널논문)
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