Bucchia, Beatrice and Heuser, Christoph (2015). Long-run variance estimation for spatial data under change-point alternatives. J. Stat. Plan. Infer., 165. S. 104 - 127. AMSTERDAM: ELSEVIER SCIENCE BV. ISSN 1873-1171
Full text not available from this repository.Abstract
In this paper, we consider the problem of estimating the long-run variance (matrix) of an R-P-valued multiparameter stochastic process {X-K}(K epsilon[1,n]d), (n, p, d epsilon N, p, d fixed) whose mean-function has an abrupt jump. We consider processes of the form X-K = Y-K + mu + I-Cn(K)Delta, where I-C is the indicator function for a set C, the change-set C-n subset of [1, n](d) is a finite union of rectangles and mu, Delta epsilon R-P are unknown parameters. The stochastic process {Y-K : K epsilon Z(d)} is assumed to fulfill a weak invariance principle. Due to the non-constant mean, kernel-type long-run variance estimators using the arithmetic mean of the observations as a mean estimator have an unbounded error for changes Delta that do not vanish for n -> infinity. To reduce this effect, we use a mean estimator which is based on an estimation of the set C-n. In the case where C-n = ([n theta(0)(1)], [n theta(0)(2)]] is a rectangle, we introduce an estimator (C) over cap (n) = ([n (theta) over cap (1)], [n (theta) over cap (2)]] and study its convergence rate. (C) 2015 Elsevier B.V. All rights reserved.
Item Type: | Journal Article | ||||||||||||
Creators: |
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URN: | urn:nbn:de:hbz:38-392569 | ||||||||||||
DOI: | 10.1016/j.jspi.2015.04.005 | ||||||||||||
Journal or Publication Title: | J. Stat. Plan. Infer. | ||||||||||||
Volume: | 165 | ||||||||||||
Page Range: | S. 104 - 127 | ||||||||||||
Date: | 2015 | ||||||||||||
Publisher: | ELSEVIER SCIENCE BV | ||||||||||||
Place of Publication: | AMSTERDAM | ||||||||||||
ISSN: | 1873-1171 | ||||||||||||
Language: | English | ||||||||||||
Faculty: | Unspecified | ||||||||||||
Divisions: | Unspecified | ||||||||||||
Subjects: | no entry | ||||||||||||
Uncontrolled Keywords: |
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URI: | http://kups.ub.uni-koeln.de/id/eprint/39256 |
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