Abstract
In recent years, several of new improved and extended probability distributions have been discovered from the current distributions to facilitate their applications in many fields. A new three-parameter distribution, the so called the T ype II half logistic Weibull (TIIHLW), is introduced for modeling lifetime data. Some mathematical properties of the TIIHLW distribution are provided. Explicit expressions for the moments, probability weighted moments, quantile function, order statistics and Renyi entropy are investigated. Maximum likelihood estimation technique is employed to estimate the model parameters and simulation issues are presented. In addition, the superiority of the subject distribution is illustrated with an application to two real data sets. Indeed, the TIIHLW model yields a better fit to these data than the beta Weibull, Mcdonald Weibull and exponentiated Weibull distributions. Keywords : Type II half logistic-G class; Weibull distribution, Order statistics; Maximum likelihood method. DOI : 10.7176/MTM/9-1-05
Highlights
The Weibull (W) distribution is a very popular distribution for modeling lifetime data in reliability where the hazard rate function is monotone
Based on the TIIHL-G family, we construct the Type II half logistic Weibull (TIIHLW) distribution as well as we provide the main statistical distributions
Some Statistical Properties This section provides some statistical properties of TIIHLW distribution
Summary
The Weibull (W) distribution is a very popular distribution for modeling lifetime data in reliability where the hazard rate function is monotone. Various generalizations and extensions of the W distribution have been proposed in the statistical literature to handle with bathtub shaped failure rates. Xie et al (2002) proposed a three- parameter modified W extension with a bathtub shaped hazard function. New generated families of continuous distributions have been attracted several statisticians to develop new models. These families are obtained by introducing one or more additional shape parameter(s) to the baseline distribution.
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