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Extremum sieve estimation in k-out-of-n systems

Extremum sieve estimation in k-out-of-n systems This article considers the non parametric estimation of absolutely continuous distribution functions of independent lifetimes of non identical components in k-out-of-n systems, 2 ⩽ k ⩽ n, from the observed “autopsy” data. In economics, ascending “button” or “clock” auctions with n heterogeneous bidders with independent private values present 2-out-of-n systems. Classical competing risks models are examples of n-out-of-n systems. Under weak conditions on the underlying distributions, the estimation problem is shown to be well-posed and the suggested extremum sieve estimator is proven to be consistent. This article considers the sieve spaces of Bernstein polynomials which allow to easily implement constraints on the monotonicity of estimated distribution functions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications in Statistics: Theory and Methods Taylor & Francis

Extremum sieve estimation in k-out-of-n systems

Extremum sieve estimation in k-out-of-n systems

Communications in Statistics: Theory and Methods , Volume 46 (10): 17 – May 19, 2017

Abstract

This article considers the non parametric estimation of absolutely continuous distribution functions of independent lifetimes of non identical components in k-out-of-n systems, 2 ⩽ k ⩽ n, from the observed “autopsy” data. In economics, ascending “button” or “clock” auctions with n heterogeneous bidders with independent private values present 2-out-of-n systems. Classical competing risks models are examples of n-out-of-n systems. Under weak conditions on the underlying distributions, the estimation problem is shown to be well-posed and the suggested extremum sieve estimator is proven to be consistent. This article considers the sieve spaces of Bernstein polynomials which allow to easily implement constraints on the monotonicity of estimated distribution functions.

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References (17)

Publisher
Taylor & Francis
Copyright
© 2017 Taylor & Francis Group, LLC
ISSN
1532-415X
eISSN
0361-0926
DOI
10.1080/03610926.2015.1091081
Publisher site
See Article on Publisher Site

Abstract

This article considers the non parametric estimation of absolutely continuous distribution functions of independent lifetimes of non identical components in k-out-of-n systems, 2 ⩽ k ⩽ n, from the observed “autopsy” data. In economics, ascending “button” or “clock” auctions with n heterogeneous bidders with independent private values present 2-out-of-n systems. Classical competing risks models are examples of n-out-of-n systems. Under weak conditions on the underlying distributions, the estimation problem is shown to be well-posed and the suggested extremum sieve estimator is proven to be consistent. This article considers the sieve spaces of Bernstein polynomials which allow to easily implement constraints on the monotonicity of estimated distribution functions.

Journal

Communications in Statistics: Theory and MethodsTaylor & Francis

Published: May 19, 2017

Keywords: Bernstein polynomials; Competing risks; k -out-of- n systems; Sieve estimation.; 62N01; 62N02; 62N05

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