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Modeling failure time data by lehman alternatives

Modeling failure time data by lehman alternatives The proportional hazards model has been extensively used in the literature to model failure time data. In this paper we propose to model failure time data by F*(f) = [F(t)]θ where F(t) is the baseline distribution function and θ is a positive real number. This model gives rise to monotonic as well as non-monotonic failure rates even though the baseline failure rate is monotonic. The monotonicity of the failure rates are studied, in general, and some order relations are examined. Some examples including exponentiated Weibull, exponential, gamma and Pareto distributions are investigated in detail. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications in Statistics: Theory and Methods Taylor & Francis

Modeling failure time data by lehman alternatives

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References (12)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1532-415X
eISSN
0361-0926
DOI
10.1080/03610929808832134
Publisher site
See Article on Publisher Site

Abstract

The proportional hazards model has been extensively used in the literature to model failure time data. In this paper we propose to model failure time data by F*(f) = [F(t)]θ where F(t) is the baseline distribution function and θ is a positive real number. This model gives rise to monotonic as well as non-monotonic failure rates even though the baseline failure rate is monotonic. The monotonicity of the failure rates are studied, in general, and some order relations are examined. Some examples including exponentiated Weibull, exponential, gamma and Pareto distributions are investigated in detail.

Journal

Communications in Statistics: Theory and MethodsTaylor & Francis

Published: Jan 1, 1998

Keywords: Monotonic and non-monotonic failure rates; exponentiated Weibull; exponentiated Pareto; exponentiated gamma; order relations

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