IMPROVED SIZER FOR TIME SERIES

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SiZer (SIgnificant ZERo crossing of the derivatives) is a scale-space visualization tool for statistical inferences. In this paper we improve global inference of SiZer for time series, originally proposed by Rondonotti, Marron and Park (2007), in two aspects. First, the estimation of the quantile in a confidence interval is theoretically justified by advanced distribution theory. Second, an improved non-parametric autocovariance function estimator is proposed using a differenced time series. A numerical study is conducted to demonstrate the sample performance of the proposed tool. In addition, asymptotic properties of SiZer for time series are investigated.
Publisher
STATISTICA SINICA
Issue Date
2009-10
Language
English
Article Type
Article
Citation

STATISTICA SINICA, v.19, no.4, pp.1511 - 1530

ISSN
1017-0405
URI
http://hdl.handle.net/10203/285763
Appears in Collection
MA-Journal Papers(저널논문)
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