Journal ArticleDOI
A new family of generalized distributions
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In this paper, a new family of generalized distributions for double-bounded random processes with hydrological applications is described, including Kw-normal, Kw-Weibull and Kw-Gamma distributions.Abstract:
Kumaraswamy [Generalized probability density-function for double-bounded random-processes, J. Hydrol. 462 (1980), pp. 79–88] introduced a distribution for double-bounded random processes with hydrological applications. For the first time, based on this distribution, we describe a new family of generalized distributions (denoted with the prefix ‘Kw’) to extend the normal, Weibull, gamma, Gumbel, inverse Gaussian distributions, among several well-known distributions. Some special distributions in the new family such as the Kw-normal, Kw-Weibull, Kw-gamma, Kw-Gumbel and Kw-inverse Gaussian distribution are discussed. We express the ordinary moments of any Kw generalized distribution as linear functions of probability weighted moments (PWMs) of the parent distribution. We also obtain the ordinary moments of order statistics as functions of PWMs of the baseline distribution. We use the method of maximum likelihood to fit the distributions in the new class and illustrate the potentiality of the new model with a...read more
Citations
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Generalized Weibull–Lindley (GWL) Distribution in Modeling Lifetime Data
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TL;DR: In this article, a new lifetime distribution named generalized Weibull-Lindley (GWL) distribution based on the T-X family of distribution was derived and investigated the shapes of its probability density function (pdf), hazard rate function, and survival function.
Journal ArticleDOI
Remarks on characterizations of Malinowska and Szynal
TL;DR: Several characterizations of Malinowska and Szynal (2008) for certain general classes of distributions are revisited and simpler proofs of them are presented.
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Generalised Odd Frechet Family of Distributions: Properties and Applications
TL;DR: In this article, a generalized odd fréchet (GOF) distribution is proposed and its properties explored, including the shape of the hazard rate function, moments, conditional moments, moment generating function, skewness, and kurtosis.
Journal ArticleDOI
Classical and Bayesian Inference on Finite Mixture of Exponentiated Kumaraswamy Gompertz and Exponentiated Kumaraswamy Fréchet Distributions under Progressive Type II Censoring with Applications
TL;DR: In this paper , a finite mixture of exponentiated Kumaraswamy Gompertz (GOM) and exponentiated kumarashamy Fréchet (Fréchet) was developed and discussed as a novel probability model.
Journal ArticleDOI
Ristić-Balakrishnan extended exponential distribution
TL;DR: In this paper, a new generalization of the extended exponential distribution, called the Ristic-Balakrishnan Extended Exponential Distribution (RED), is introduced and studied.
References
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Statistical Theory of Reliability and Life Testing Probability Models
Book
Statistical Theory of Reliability and Life Testing: Probability Models
Richard E. Barlow,Frank Proschan +1 more
TL;DR: A number of new classes of life distributions arising naturally in reliability models are treated systematically and each provides a realistic probabilistic description of a physical property occurring in the reliability context, thus permitting more realistic modeling of commonly occurring reliability situations.
Journal ArticleDOI
L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics
TL;DR: The authors define L-moments as the expectations of certain linear combinations of order statistics, which can be defined for any random variable whose mean exists and form the basis of a general theory which covers the summarization and description of theoretical probability distributions.
Journal Article
A class of distributions which includes the normal ones
TL;DR: In this paper, a nouvelle classe de fonctions de densite dependant du parametre de forme λ, telles que λ=0 corresponde a la densite normale standard.
Journal ArticleDOI
Generalized additive models for location, scale and shape
TL;DR: The generalized additive model for location, scale and shape (GAMLSS) as mentioned in this paper is a general class of statistical models for a univariate response variable, which assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects.
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