Ayman Alzaatreh

Weibull-pareto distribution and its applications

In this article, a new distribution, namely, Weibull-Pareto distribution is defined and studied. Various properties of the Weibull-Pareto distribution are obtained. The distribution is found to be unimodal and the shape of the distribution can be skewed to the right or skewed to the left. Results for moments, limiting behavior, and Shannons entropy are provided. The method of modified maximum likelihood estimation is proposed for estimating the model parameters. Several real data sets are used to illustrate the applications of Weibull-Pareto distribution.

The gamma-normal distribution: Properties and applications

In this paper, some properties of gamma-X family are discussed and a member of the family, the gamma-normal distribution, is studied in detail. The limiting behaviors, moments, mean deviations, dispersion, and Shannon entropy for the gamma-normal distribution are provided. Bounds for the non-central moments are obtained. The method of maximum likelihood estimation is proposed for estimating the parameters of the gamma-normal distribution. Two real data sets are used to illustrate the applications of the gamma-normal distribution.

The logistic-X family of distributions and its applications

ABSTRACT The logistic distribution has a prominent role in the theory and practice of statistics. We introduce a new family of continuous distributions generated from a logistic random variable called the logistic-X family. Its density function can be symmetrical, left-skewed, right-skewed, and reversed-J shaped, and can have increasing, decreasing, bathtub, and upside-down bathtub hazard rates shaped. Further, it can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. We derive explicit expressions for the ordinary and incomplete moments, quantile and generating functions, Bonferroni and Lorenz curves, Shannon entropy, and order statistics. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. We also investigate the properties of one special model, the logistic-Fréchet distribution, and illustrate its importance by means of two applications to real data sets.

T-normal family of distributions: a new approach to generalize the normal distribution

The idea of generating skewed distributions from normal has been of great interest among researchers for decades. This paper proposes four families of generalized normal distributions using the T-X framework. These four families of distributions are named as T-normal families arising from the quantile functions of (i) standard exponential, (ii) standard log-logistic, (iii) standard logistic and (iv) standard extreme value distributions. Some general properties including moments, mean deviations and Shannon entropy of the T-normal family are studied. Four new generalized normal distributions are developed using the T-normal method. Some properties of these four generalized normal distributions are studied in detail. The shapes of the proposed T-normal distributions can be symmetric, skewed to the right, skewed to the left, or bimodal. Two data sets, one skewed unimodal and the other bimodal, are fitted by using the generalized T-normal distributions.AMS 2010 Subject Classification60E05; 62E15; 62P10

A New Weibull–Pareto Distribution: Properties and Applications

Many distributions have been used as lifetime models. In this article, we propose a new three-parameter Weibull–Pareto distribution, which can produce the most important hazard rate shapes, namely, constant, increasing, decreasing, bathtub, and upsidedown bathtub. Various structural properties of the new distribution are derived including explicit expressions for the moments and incomplete moments, Bonferroni and Lorenz curves, mean deviations, mean residual life, mean waiting time, and generating and quantile functions. The Rényi and q entropies are also derived. We obtain the density function of the order statistics and their moments. The model parameters are estimated by maximum likelihood and the observed information matrix is determined. The usefulness of the new model is illustrated by means of two real datasets on Wheaton river flood and bladder cancer. In the two applications, the new model provides better fits than the Kumaraswamy–Pareto, beta-exponentiated Pareto, beta-Pareto, exponentiated Pareto, and Pareto models.

A New Extension of Generalized Exponential Distribution with Application to Ozone Data

ABSTRACT In this article, a new distribution, called transformed Generalized Exponential Distribution () is proposed. The distribution has simple closed forms pdf and cdf. Several properties of the distribution are studied including unimodality, moments, conditional moments, stochastic ordering and Shannon entropy. The distribution is capable of monotonically increasing, decreasing, bathtub and upside down shaped hazard rates. Furthermore, we discussed several methods of estimation for estimating the parameters. In particular, we compare, through simulation study, the performance of the maximum likelihood estimation, maximum product spacing estimation, ordinary and weighted least-squares estimation methods. Also, the average interval lengths and coverage rates of 95% confidence intervals, including boot-p and boot-t confidence intervals for different parameter values are provided. A real data set is used to illustrate the applicability of the proposed distribution.

The Poisson-X family of distributions

ABSTRACT Recently, Ristić and Nadarajah [A new lifetime distribution. J Stat Comput Simul. 2014;84:135–150] introduced the Poisson generated family of distributions and investigated the properties of a special case named the exponentiated-exponential Poisson distribution. In this paper, we study general mathematical properties of the Poisson-X family in the context of the T-X family of distributions pioneered by Alzaatreh et al. [A new method for generating families of continuous distributions. Metron. 2013;71:63–79], which include quantile, shapes of the density and hazard rate functions, asymptotics and Shannon entropy. We obtain a useful linear representation of the family density and explicit expressions for the ordinary and incomplete moments, mean deviations and generating function. One special lifetime model called the Poisson power-Cauchy is defined and some of its properties are investigated. This model can have flexible hazard rate shapes such as increasing, decreasing, bathtub and upside-down bathtub. The method of maximum likelihood is used to estimate the model parameters. We illustrate the flexibility of the new distribution by means of three applications to real life data sets.

Family of generalized gamma distributions: Properties and applications

In this paper, a family of generalized gamma distributions, T-gamma family, has been proposed using the T-R{Y} framework. The family of distributions is generated using the quantile functions of uniform, exponential, log-logistic, logistic and extreme value distributions. Several general properties of the T-gamma family are studied in details including moments, mean deviations, mode and Shannon’s entropy. Three new generalizations of the gamma distribution which are members of the T-gamma family are developed and studied. The distributions in the T-gamma family are very flexible due to their various shapes. The distributions can be symmetric, skewed to the right, skewed to the left, or bimodal. Four data sets with various shapes are fitted by using members of the T-gamma family of distributions

Alpha power Weibull distribution: Properties and applications

ABSTRACT In this paper, a new lifetime distribution is defined and studied. We refer to the new distribution as alpha power Weibull distribution. The importance of the new distribution comes from its ability to model monotone and non monotone failure rate functions, which are quite common in reliability studies. Various properties of the proposed distribution are obtained including moments, quantiles, entropy, order statistics, mean residual life function, and stress-strength parameter. The maximum likelihood estimation method is used to estimate the parameters. Two real data sets are used to illustrate the importance of the proposed distribution.

A study of the Gamma-Pareto (IV) distribution and its applications

ABSTRACT Pareto distributions and their close relatives and generalizations provide very flexible families of heavy-tailed distributions that may be used to model income distributions as well as a wide variety of other social and economic distributions. On the other hand, gamma distribution has a wide application in various social and economic spheres such as survival analysis, to model aggregate insurance claims, and the amount of rainfall accumulated in a reservoir etc. Combining the above two heavy-tailed distributions, using the technique by Alzaatreh et al. (2012), we define a new distribution, namely Gamma-Pareto (IV) distribution, hereafter called as GPD(IV) distribution. Various properties of the GPD(IV) are investigated such as limiting behavior, moments, mode, and Shannon entropy. Also some characterizations of the GPD(IV) distribution are mentioned in this paper. Maximum likelihood method is proposed for estimating the model parameters. For illustrative purposes, real data sets are considered as applications of the GPD(IV) distribution.