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M-estimation for dependent random variables


Furrer, R (2002). M-estimation for dependent random variables. Statistics and Probability Letters, 57(4):337-341.

Abstract

This paper discusses the consistency in the strong sense and essential uniqueness of M-estimation for dependent random variables. The hypotheses are based on the function defining implicitly the M-estimation as well as on its first derivative and its Hessian matrix. No explicit hypotheses on the random variables are necessary for consistency and uniqueness, thus the framework holds for any stochastic process.

This paper discusses the consistency in the strong sense and essential uniqueness of M-estimation for dependent random variables. The hypotheses are based on the function defining implicitly the M-estimation as well as on its first derivative and its Hessian matrix. No explicit hypotheses on the random variables are necessary for consistency and uniqueness, thus the framework holds for any stochastic process.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Language:English
Date:May 2002
Deposited On:27 Aug 2010 12:07
Last Modified:05 Apr 2016 13:25
Publisher:Elsevier
ISSN:0167-7152
Publisher DOI:10.1016/S0167-7152(02)00084-6
Permanent URL: http://doi.org/10.5167/uzh-21948

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