Statistical Science

The Impact of Bootstrap Methods on Time Series Analysis

Dimitris N. Politis

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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.

Article information

Source
Statist. Sci., Volume 18, Issue 2 (2003), 219-230.

Dates
First available in Project Euclid: 19 September 2003

Permanent link to this document
https://projecteuclid.org/euclid.ss/1063994977

Digital Object Identifier
doi:10.1214/ss/1063994977

Mathematical Reviews number (MathSciNet)
MR2026081

Zentralblatt MATH identifier
1332.62340

Keywords
Block bootstrap confidence intervals linear models resampling large sample inference nonparametric estimation subsampling

Citation

Politis, Dimitris N. The Impact of Bootstrap Methods on Time Series Analysis. Statist. Sci. 18 (2003), no. 2, 219--230. doi:10.1214/ss/1063994977. https://projecteuclid.org/euclid.ss/1063994977


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