On Generalized Inverse Pareto Family of Distributions: Properties and Applications

Oregon Convention Center 

This study proposes new families of generalized inverse Pareto distributions using the T-R{Y} framework. Different choices for the distributions of the random variables T and Y lead to generalized families of the random variable R, which, in this study, is characterized by the inverse Pareto distribution. The generalized family of distributions is thus named as T-inverse Pareto{Y} family. We consider the exponential, Weibull, log-logistic, logistic, Cauchy, and extreme value distribution as potential choices for the distribution of the random variable Y. Specific members of the T-inverse Pareto{Y} family exhibit symmetric, skewed to the right, skewed to the left, unimodal, or bimodal density functions. Some statistical properties of the T-inverse Pareto{Y} family are investigated. The method of maximum likelihood is proposed for estimating the distribution parameters and its performance is assessed using a simulation study. Four real datasets from different disciplines are analyzed to demonstrate the flexibility of the proposed T-inverse Pareto{Y} family of distributions.

T-R{Y} framework

Inverse Pareto distribution

Quantile function

Maximum likelihood estimation

Censoring 

Section on Statistical Computing