article

Generalized Likelihood Ratio Test and Cox's F-Test Based on Fuzzy Lifetime Data

Authors:

Muhammad Shafiq,

Reinhard ViertlAuthors Info & Claims

International Journal of Intelligent Systems, Volume 32, Issue 1

Pages 3 - 16

https://doi.org/10.1002/int.21825

Published: 01 January 2017 Publication History

Abstract

Recent developments in measurement science show that continuous measurements are no more precise numbers but more or less imprecise and are called fuzzy. Therefore, to utilize this imprecision of observations, the corresponding analysis techniques related to continuous quantities are essential to generalize fuzzy observations. This study is aimed to generalize the likelihood ratio test and Cox's F-test for fuzzy observations in such a way that they are able to integrate fuzziness of lifetime observations for the inference in addition to stochastic variation. The proposed generalized tests are best suited for lifetime analysis as these cover fuzziness of single observations in addition to random variation.

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Cited By

Boškoski PPerne MRameša MBoshkoska B(2021)Variational Bayes survival analysis for unemployment modellingKnowledge-Based Systems10.1016/j.knosys.2021.107335229:COnline publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107335
Chukhrova NJohannssen A(2021)Generalized two‐tailed hypothesis testing for quantiles applied to the psychosocial status during the COVID‐19 pandemicInternational Journal of Intelligent Systems10.1002/int.2259236:12(7412-7442)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1002/int.22592
Chukhrova NJohannssen A(2021)Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID‐19International Journal of Intelligent Systems10.1002/int.2240736:6(2922-2963)Online publication date: 27-Apr-2021
https://dl.acm.org/doi/10.1002/int.22407
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Generalized Likelihood Ratio Test and Cox's F-Test Based on Fuzzy Lifetime Data
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cover image International Journal of Intelligent Systems

International Journal of Intelligent Systems Volume 32, Issue 1

January 2017

103 pages

ISSN:0884-8173

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 01 January 2017

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Cited By

Boškoski PPerne MRameša MBoshkoska B(2021)Variational Bayes survival analysis for unemployment modellingKnowledge-Based Systems10.1016/j.knosys.2021.107335229:COnline publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107335
Chukhrova NJohannssen A(2021)Generalized two‐tailed hypothesis testing for quantiles applied to the psychosocial status during the COVID‐19 pandemicInternational Journal of Intelligent Systems10.1002/int.2259236:12(7412-7442)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1002/int.22592
Chukhrova NJohannssen A(2021)Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID‐19International Journal of Intelligent Systems10.1002/int.2240736:6(2922-2963)Online publication date: 27-Apr-2021
https://dl.acm.org/doi/10.1002/int.22407
Gallego FCorchuelo R(2020)A deep-learning approach to mining conditionsKnowledge-Based Systems10.1016/j.knosys.2019.105422193:COnline publication date: 6-Apr-2020
https://dl.acm.org/doi/10.1016/j.knosys.2019.105422

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