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Jump or kink: note on super-efficiency in segmented linear regression break-point estimation

Chen, Yining (2020) Jump or kink: note on super-efficiency in segmented linear regression break-point estimation. Biometrika. ISSN 0006-3444 (In Press)

[img] Text (Jump or kink) - Accepted Version
Pending embargo until 1 January 2100.

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Identification Number: 10.1093/biomet/asaa049

Abstract

We consider the problem of segmented linear regression with a single break-point, with the focus on estimating of the location of the break-point. Let n be the sample size, we show that the global minimax convergence rate for this problem in terms of the mean absolute error is O(n^{−1/3}). On the other hand, we demonstrate the construction of a super-efficient estimator that achieves the pointwise convergence rate of either O(n^{−1}) or O(n^{−1/2}) for every fixed parameter value, depending on whether the structural change is a jump or a kink. The implications of this example and a potential remedy are discussed.

Item Type: Article
Official URL: https://academic.oup.com/biomet
Additional Information: © 2020 Biometrika Trust
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 19 Feb 2020 09:45
Last Modified: 17 Jul 2020 09:15
URI: http://eprints.lse.ac.uk/id/eprint/103488

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