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Activity Number: 16
Type: Topic Contributed
Date/Time: Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #303632
Title: Improved SiZer for Time Series
Author(s): Cheolwoo Park*+ and Jan Hannig and Kee-Hoon Kang
Companies: The University of Georgia and The University of North Carolina at Chapel Hill and Hankuk University of Foreign Studies
Address: 101 Cedar St., Athens, GA, 30602,
Keywords: Autocovariance function estimation ; Local linear smoothing ; Multiple testing adjustment ; SiZer ; Statistical convergence ; Time series
Abstract:

SiZer (SIgnificant ZERo crossing of the derivatives) is a scale-space visualization tool for statistical inferences. In this work we improve global inference of SiZer for time series, which was 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 nonparametric 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.

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