Journal of Statistical Theory and Applications 

About: Journal of Statistical Theory and Applications is an academic journal published by Atlantis Press. The journal publishes majorly in the area(s): Order statistic & Estimator. It has an ISSN identifier of 1538-7887. It is also open access. Over the lifetime, 325 publications have been published receiving 1838 citations. The journal is also known as: JSTA.

The Burr X Generator of Distributions for Lifetime Data

Abstract: The statistical literature contains many new classes of distributions which have been constructed by extending common families of continuous distributions by means of adding one or more shape parameters. The inducted extra parameter(s) to the existing probability distribution have been shown to improve the flexibility and goodness of fits of the distribution against the intuition of model parsimony. Therefore, many methods of adding a parameter to distributions have been proposed by several researchers and these new families have been used for modeling data in many applied areas such as engineering, economics, biological studies, environmental sciences and many more. In fact the modern computing technology has made many of these techniques accessible if the analytical solutions are very complicated. Gupta et al. [18] defined the exponentiated-G (exp-G) class, which consists of raising the cumulative distribution function (cdf) to a positive power parameter and proposed the exponentiated exponential (EE) distribution, defined by the cdf (for x > 0) F(x) = [1− exp(−λx)]θ , where λ ,θ > 0. This equation is simply the θ th power of the standard exponential cumulative distribution. Many Journal of Statistical Theory and Applications, Vol. 16, No. 3 (September 2017) 288–305 ___________________________________________________________________________________________________________

The Kumaraswamy Pareto distribution

Abstract: The modeling and analysis of lifetimes is an important aspect of statistical work in a wide variety of scientific and technological fields. For the first time, the called Kumaraswamy Pareto distribution, is introduced and studied. The new distribution can have a decreasing and upside-down bathtub failure rate function depending on the values of its parameters. It includes as special sub-models the Pareto and exponentiated Pareto (Gupta et al., 1998) distributions. Some structural properties of the proposed distribution are studied including explicit expressions for the moments and generating function. We provide the density function of the order statistics and obtain their moments. The method of maximum likelihood is used for estimating the model parameters and the observed information matrix is derived. A real data set is used to compare the new model with widely known distributions.

A New Lifetime Model: The Gamma Extended Fréchet Distribution

Abstract: For the first time, a four-parameter lifetime model, called the gamma extended Frechet distribution, is defined and studied. We obtain some of its mathematical properties. Explicit expressions for the ordinary and incom- plete moments, quantile function, mean deviations, Renyi entropy and reliability are provided. The order statis- tics and their moments are derived. The method of maximum likelihood is used for estimating the model parameters. We determine the observed information matrix. An application to a real data set shows that the new distribution can provide a better fit than other classical lifetime models. We hope that this generalization may attract wider applications in reliability, biology and survival analysis.

The Exponential Lindley Odd Log-Logistic-G Family: Properties, Characterizations and Applications

Abstract: A new family of distributions called the exponential Lindley odd log-logistic G family is introduced and studied. The new generator generalizes three newly defined G families and also defines two new G families. We provide some mathematical properties of the new family. Characterizations based on truncated moments as well as in terms of the hazard function are presented. The maximum likelihood is used for estimating the model parameters. We assess the performance of the maximum likelihood estimators in terms of biases and mean squared errors by means of a simulation study. Finally, the usefulness of the family is illustrated by means of three real data sets. The new model provides consistently better fits than other competitive models for these data sets.