Transmuted Lindley-Geometric Distribution and its applications

A functional composition of the cumulative distribution function of one probability distribution with the inverse cumulative distribution function of another is called the transmutation map. In this article, we will use the quadratic rank transmutation map (QRTM) in order to generate a flexible family of probability distributions taking Lindley geometric distribution as the base value distribution by introducing a new parameter that would offer more distributional flexibility. It will be shown that the analytical results are applicable to model real world data.Comment: 20 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1309.326

Another Generalized Transmuted Family of Distributions: Properties and Applications

We introduce and study general mathematical properties of a new generator of continuous distributions with two extra parameters called the Another generalized transmuted family of distributions. We present some special models. We investigate the asymptotes and shapes. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. We obtain explicit expressions for the ordinary and incomplete moments and generating functions, Bonferroni and Lorenz curves, asymptotic distribution of the extreme values, Shannon and Renyi entropies and order statistics, which hold for any baseline model, certain characterisations are presented. Further, we introduce a bivariate extensions of the new family. We discuss the dierent method of estimation of the model parameters and illustrate the potentiality of the family by means of two applications to real data. A brief simulation for evaluating Maximum likelihood estimator is done

Some Completely Monotonic Properties for the (p, g)-Gamma Function

MSC 2010: 33B15, 26A51, 26A4

Generalized Transmuted Family of Distributions: Properties and Applications

We introduce and study general mathematical properties of a new generator of continuous distributions with two extra parameters called the Generalized Transmuted Family of Distributions. We investigate the shapes and present some special models. The new density function can be expressed as a linear combination of exponentiated densities in terms of the same baseline distribution. We obtain explicit expressions for the ordinary and incomplete moments and generating function, Bonferroni and Lorenz curves, asymptotic distribution of the extreme values, Shannon and R´enyi entropies and order statistics, which hold for any baseline model. Further, we introduce a bivariate extension of the new family. We discuss the different methods of estimation of the model parameters and illustrate the potential application of the model via real data. A brief simulation for evaluating Maximum likelihood estimator is done. Finally certain characterziations of our model are presented

Logarithmically completely monotonic functions involving the Generalized Gamma Function

By a simple approach, two classes of functions involving generalization Euler's gamma function and originating from certain  problems of traffic flow are proved to be logarithmically  completely monotonic and a class of functions involving the psi function is showed to be completely monotonic

A new generalized Lindley distribution

In this paper, we present a new class of distributions called New Generalized Lindley Distribution(NGLD). This class of distributions contains several distributions such as gamma, exponential and Lindley as special cases. The hazard function, reverse hazard function, moments and moment generating function and inequality measures are are obtained. Moreover, we discuss the maximum likelihood estimation of this distribution. The usefulness of the new model is illustrated by means of two real data sets. We hope that the new distribution proposed here will serve as an alternative model to other models available in the literature for modelling positive real data in many areas. Keywords: Generalized Lindley Distribution; Gamma distribution, Maximum likelihood estimation; Moment generating function

Some properties of Gamma Burr type X distribution with application

We develop a new continuous distribution called the Gamma-Burr type X (GBX) distribution that extends the Burr type X distribution that has increasing, decreasing and bathtub shapes for the hazard function. Various structural properties of this new distribution are provide, that includes the limit behavior, Quantile function and sub-models. From the generalization of the probability density function and cumulative distribution function of this distribution, the expression for the rth moment, moment generating function, Rényi entropy, and the order statistics can be established. We considered the maximum likelihood estimation to estimate the parameters. A real data set is applied to illustrate the usefulness of the GBX distribution. This new distribution will serve as an alternative model to other models available in the literature for modeling positive real data in many areas