The Use of Conditional Probability Integral Transformation Method for Testing Accelerated Failure Time Models
This paper suggests the use of the conditional probability integral transformation (CPIT) method as a goodness of fit (GOF) technique in the field of accelerated life testing (ALT), specifically for validating the underlying distributional assumption in accelerated failure time (AFT) model. The meth...
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| Veröffentlicht in: | Pakistan journal of statistics and operation research 2016-01, Vol.12 (2), p.369 |
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| creator | Abdel-Ghaly, Abdalla Ahmed Aly, Hanan Mohamed Abde-Rahman, Elham Abdel-Malik |
| description | This paper suggests the use of the conditional probability integral transformation (CPIT) method as a goodness of fit (GOF) technique in the field of accelerated life testing (ALT), specifically for validating the underlying distributional assumption in accelerated failure time (AFT) model. The method is based on transforming the data into independent and identically distributed (i.i.d) Uniform (0, 1) random variables and then applying the modified Watson statistic to test the uniformity of the transformed random variables. This technique is used to validate each of the exponential, Weibull and lognormal distributions' assumptions in AFT model under constant stress and complete sampling. The performance of the CPIT method is investigated via a simulation study. It is concluded that this method performs well in case of exponential and lognormal distributions. Finally, a real life example is provided to illustrate the application of the proposed procedure. |
| doi_str_mv | 10.18187/pjsor.v12i2.1035 |
| format | Article |
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| subjects | Conditional probability Constants Failure times Integral transformations Pakistan Random variables Sampling Test procedures |
| title | The Use of Conditional Probability Integral Transformation Method for Testing Accelerated Failure Time Models |
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