Additive uniform exponential distribution (original) (raw)

This paper focuses on the Two Component Mixture of Additive Uniform Exponential Distribution which is an extension of additive uniform exponential distribution proposed by venkata subbarao uppu (2010). The various distributional properties like, mean, variance, moment generating function, recurrence relation of moments, skewness, kurtosis, etc., are discussed. Some inferential aspects of this distribution are presented.

In this paper a new probability density function with bounded domain is presented. This distribution arises from the Marshall–Olkin extended exponential distribution proposed by Marshall and Olkin (1997). It depends on two parameters and can be considered as an alternative to the classical beta and Kumaraswamy distributions. It presents the advantage of not including any additional parameter(s) or special function in its formulation. The new transformed model, called the unit-Marshall–Olkin extended exponential (UMOEE) distribution which exhibits decreasing, increasing and then bathtub shaped density while the hazard rate has increasing and bathtub shaped. Various properties of the distribution (including quantiles, ordinary moments, incomplete moments, conditional moments, moment generating function, conditional moment generating function, hazard rate function, mean residual lifetime, Rényi and δ-entropies, stress-strength reliability, order statistics and distributions of sums, di...

The exponential distribution is a popular statistical distribution to study the problems in lifetime and reliability theory. We proposed a new generalized exponential distribution, wherein exponentiated exponential and exponentiated generalized exponential distributions are sub-models of the proposed distribution. We study several important statistical and mathematical properties of the newly developed model and provide the simple expressions for the generating function, moments and mean deviations. Parameters of the proposed distribution are estimated by the technique of maximum likelihood. For two real data sets from the field of biology and engineering, the proposed distribution is compared to some existing distributions. It is found that the proposed model is more suitable and useful to study lifetime data. Thus, it gives us another alternative model for existing models.

The linear exponential distribution is a very well-known distribution for modeling lifetime data in reliability and medical studies. We introduce in this paper a new four-parameter generalized version of the linear exponential distribution which is called Kumaraswamy linear exponential distribution. We provide a comprehensive account of the mathematical properties of the new distributions. In particular, a closed-form expressions for the density, cumulative distribution and hazard rate function of the distribution is given. Also, the rth order moment and moment generating function are derived. The maximum likelihood estimation of the unknown parameters is discussed.

Accepted for publication in Journal of data science: JDS · March 2016