Robinson, Peter M.
(1997)
Large-sample inference for nonparametric regression with dependent errors.
Annals of Statistics, 25 (5).
pp. 2054-2083.
ISSN 0090-5364
Abstract
A central limit theorem is given for certain weighted partial sums of a covariance stationary process, assuming it is linear in martingale differences, but without any restriction on its spectrum. We apply the result to kernel nonparametric fixed-design regression, giving a single central limit theorem which indicates how error spectral behavior at only zero frequency influences the asymptotic distribution and covers long-range, short-range and negative dependence. We show how the regression estimates can be Studentized in the absence of previous knowledge of which form of dependence pertains, and show also that a simpler Studentization is possible when long-range dependence can be taken for granted.
| Item Type: |
Article
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| Official URL: |
http://www.imstat.org/aos/ |
| Additional Information: |
Published 1997 © Institute of Mathematical Statistics. LSE has developed LSE Research Online so that users may access research output of the School. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in LSE Research Online to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain. You may freely distribute the URL (http://eprints.lse.ac.uk) of the LSE Research Online website. |
| Divisions: |
Economics |
| Subjects: |
H Social Sciences > HA Statistics |
| Date Deposited: |
15 Feb 2008 |
| Last Modified: |
11 Dec 2024 22:05 |
| URI: |
http://eprints.lse.ac.uk/id/eprint/302 |
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