On the survival models for step-stress experiments based on fuzzy life time data

On the survival models for step-stress experiments based on fuzzy life time data In statistical methodologies of life time analyses accelerated life testing (ALT) has a significant importance. In accelerated life testing the measurements of life times are recorded under various conditions which are more severe than usual environment. The techniques related to the inference of life times in ALT are usually based on precise measurements. In practical applications life time data have two types of uncertainty, one is stochastic variation and the other is fuzziness. Classical stochastic models are developed to draw inference based on the variation among observations, and do nothing with fuzziness. By doing so the analyses are based on incomplete information and can lead to misleading conclusions. In this study estimators are proposed to cover fuzziness in addition to stochastic variation of the life times. The results based on the proposed methods are more suitable for realistic life time data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

On the survival models for step-stress experiments based on fuzzy life time data

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Publisher
Springer Netherlands
Copyright
Copyright © 2015 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-015-0295-9
Publisher site
See Article on Publisher Site

Abstract

In statistical methodologies of life time analyses accelerated life testing (ALT) has a significant importance. In accelerated life testing the measurements of life times are recorded under various conditions which are more severe than usual environment. The techniques related to the inference of life times in ALT are usually based on precise measurements. In practical applications life time data have two types of uncertainty, one is stochastic variation and the other is fuzziness. Classical stochastic models are developed to draw inference based on the variation among observations, and do nothing with fuzziness. By doing so the analyses are based on incomplete information and can lead to misleading conclusions. In this study estimators are proposed to cover fuzziness in addition to stochastic variation of the life times. The results based on the proposed methods are more suitable for realistic life time data.

Journal

Quality & QuantitySpringer Journals

Published: Nov 30, 2015

References

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