Study on fitting probability models to survival data
DOI:
https://doi.org/10.33448/rsd-v11i5.28430Keywords:
Parameter estimation; Goodness of fit; Probability distribution.Abstract
In this article, probability distribution models known in the literature are used. The aim of the present study is to search some probability models with better fit in two specific data sets in survival analysis. The first one refers to the resistance of ball bearings, and the second to the period of successive failures of the air conditioning system of a fleet of Air Boeing aircraft. The estimation of the parameters is performed using the maximum likelihood method. An application to two data sets is given to illustrate the veracity of the fits. The distributions that showed great results were Exponentialized Exponential, Exponential Burr XII, Gdus Exponentialized and Dagum, for the first analysis. For the second analysis, the Exponentialized Weibull, Kumaraswamy Weibull, Kumaraswamy Burr XII and Dagum. The Akaike Information Criterion, Bayesian Information Criterion, Anderson Darling and Cramér von Mises are used as comparison measures. The analysis will be done with the help of the package (AdequacyModel) in the R software.
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