Abstract
In a recent paper, a new model called the Exponentiated Log-Logistic Weibull (ELLoGW) distribution with applications to reliability, survival analysis and income data was proposed. In this study, we applied the recently developed ELLoGW model to a wide range of censored data. We found that the ELLoGW distribution is a very competitive model for describing censored observations in life-time reliability problems such as survival analysis. This work shows that in certain cases, the ELLoGW distribution performs better than other parametric model such as the Log-Logistic Weibull, Exponentiated Log-Logistic Exponential, Log-Logistic Exponential distributions and the non-nested Gamma-Dagum (GD).
Highlights
Oluyede et al had recently proposed a new model called the Exponentiated Log-Logistic Weibull (ELLoGW) distribution with applications to reliability, survival analysis and income data [1]
The ELLoGW distribution is fitted to the data sets and these fits are compared to the fits using exponentiated log-logistic exponential (ELLoGE), log-logistic
We can conclude that there are no significant differences between fits of the LLoGW and ELLoGW distributions, as well as between fits of the Exponentiated Log-Logistic Exponential (ELLoGE) and ELLoGW distributions
Summary
Oluyede et al had recently proposed a new model called the Exponentiated Log-Logistic Weibull (ELLoGW) distribution with applications to reliability, survival analysis and income data [1]. We are primarily concerned with the application of the new model (ELLoGW) to censored data. The samples could be electrical or mechanical components, system, individuals, or perhaps computer chips in a reliability experiment. It could be patients, subjects or individuals that are recruited for drug or clinical trials. In most cases, such experiments are terminated before all the samples could have “failed”. A type I right censored sample (t1, ε1), ..., (tn, εn) from the ELLoGW distribution with pdf gELLoGW (.) and survival function S ELLoGW (.) can be written as n
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