Odd Log-logistic Distribution Research Articles

Sampling Plan for Lab Extracted Beverages Data with Multiple Dependent States Using the Odd Log-Logistic Distribution

Sampling Plan for Lab Extracted Beverages Data with Multiple Dependent States Using the Odd Log-Logistic Distribution

The Odd Log-Logistic Transmuted-G Family of Distributions: Properties, Characterization, Applications and Different Methods of Estimation

In this work, we propose a new class of lifetime distributions called the odd log-logistic transmuted-G family. The proposed family of distributions is constructed by compounding the odd log-logistic distribution with the transmuted distribution. It can provide better fits than some of the known lifetime models and this fact represents a good characterization of this new family. Some characterizations for the new family are presented as well as some of its mathematical properties including. The maximum likelihood, Least squares and weighted least squares, Cram\'{e}r--von--Mises, Anderson-Darling and right-tailed Anderson-Darlingare and maximum product of spacings methods are used for estimating the model parameters. The importance and flexibility of the new family are illustrated by means of an application to a real data set.

The Odd Log-Logistic Log-Normal Distribution with Theory and Applications

In this paper, a new heavy-tailed distribution is used to model data with a strong right tail, as often occuring in practical situations. The proposed distribution is derived from the log-normal distribution, by using odd log-logistic distribution. Statistical properties of this distribution, including hazard function, moments, quantile function, and asymptotics, are derived. The unknown parameters are estimated by the maximum likelihood estimation procedure. For different parameter settings and sample sizes, a simulation study is performed and the performance of the new distribution is compared to beta log-normal. The new lifetime model can be very useful and its superiority is illustrated by means of two real data sets.