Dependent SiZer: goodness-of-fit tests for tune series models

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In this paper, we extend SiZer (SIgnificant ZERo crossing of the derivatives) to dependent data for the purpose of goodness-of-fit tests for time series models. Dependent SiZer compares the observed data with a specific null model being tested by adjusting the statistical inference using an assumed autocovariance function. This new approach uses a SiZer type visualization to flag statistically significant differences between the data and a given null model. The power of this approach is demonstrated through some examples of time series of Internet traffic data. It is seen that such time series can have even more burstiness than is predicted by the popular, long-range dependent, Fractional Gaussian Noise model. © 2004 Taylor & Francis Ltd.
Publisher
CARFAX PUBLISHING
Issue Date
2004-10
Language
English
Article Type
Article
Citation

JOURNAL OF APPLIED STATISTICS, v.31, no.8, pp.999 - 1017

ISSN
0266-4763
DOI
10.1080/0266476042000270554
URI
http://hdl.handle.net/10203/285746
Appears in Collection
MA-Journal Papers(저널논문)
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