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The Use of Conditional Probability Integral Transformation Method for Testing Accelerated Failure Time Models


Accelerated life testing, Accelerated failure time model, Constant stress, Goodness of fit techniques, Conditional probability integral transformation method.


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) UniformĀ (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.





Pakistan Journal of Statistics and Operation Research; Vol. 12 No. 2, 2016

Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810