You are not currently logged in.
Access your personal account or get JSTOR access through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. 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
Dimitris N. Politis
Vol. 18, No. 2, Silver Anniversary of the Bootstrap (May, 2003), pp. 219-230
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/3182852
Page Count: 12
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.
Preview not available
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.
Statistical Science © 2003 Institute of Mathematical Statistics