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The Impact of Bootstrap Methods on Time Series Analysis

Dimitris N. Politis
Statistical Science
Vol. 18, No. 2, Silver Anniversary of the Bootstrap (May, 2003), pp. 219-230
Stable URL: http://www.jstor.org/stable/3182852
Page Count: 12
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
The Impact of Bootstrap Methods on Time Series Analysis
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Abstract

Sparked by Efron's seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data-mainly i.i.d. or regression set-ups. By contrast, in the 1990s much research was directed towards resampling dependent data, for example, time series and random fields. Consequently, the availability of valid nonparametric inference procedures based on resampling and/or subsampling has freed practitioners from the necessity of resorting to simplifying assumptions such as normality or linearity that may be misleading.

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