Author(s):
1. Abdalla Abdel-Ghaly:
Faculty of Economics and Political Science
Department of Statistics, Cairo University Eygpt.
2. Hanan Aly:
Faculty of Economics and Political Science
Department of Statistics, Cairo University, Eygpt.
3. Elham Abdel-Rahman:
Faculty of Economics and Political Science
Department of Statistics, Cairo University, Eygpt.
Abstract:
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) models. The CPIT method is based on transforming the data into independent and identically distributed (i.i.d) Uniform (0, 1) random variables and then applying a certain GOF technique to test the uniformity of the transformed random variables. In this paper, the CPIT method is used to validate each of the exponential and lognormal distributions' assumptions in an AFT model under constant stress and complete sampling. The performance of this method is investigated via a simulation study. Moreover, a real life example is presented to illustrate the application of it. Concluding comments about the good performance of the CPIT method are made.
Page(s):
369-387
DOI:
DOI not available
Published:
Journal: Pakistan Journal of Statistics and Operation Research, Volume: 12, Issue: 2, Year: 2016
Keywords:
Accelerated life testing
,
Accelerated failure time model
,
Conditional probability integral transformation method
,
Constant stress
,
Goodness of fit techniques
References:
References are not available for this document.
Citations
Citations are not available for this document.