Journal ArticleDOI
A new family of generalized distributions
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TLDR
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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Journal ArticleDOI
The Kumaraswamy Pareto distribution
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Journal ArticleDOI
The Lindley family of distributions: properties and applications
Selen Cakmakyapan,Gamze Ozel +1 more
TL;DR: In this paper, a new class of distributions called the Lindley generator with one extra parameter to generate many continuous distributions is proposed. But the authors do not discuss the maximum likelihood method to estimate model parameters, and the importance of the new generator is illustrated by means of three real data sets.
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The new class of Kummer beta generalized distributions
Rodrigo R. Pescim,Gauss M. Cordeiro,Clarice Garcia Borges Demétrio,Edwin M. M. Ortega,Saralees Nadarajah +4 more
TL;DR: The Kummer beta generalized family as mentioned in this paper is a family of distributions that extends the normal, Weibull, gamma and Gumbel distributions, among several other well-known distributions.
Journal ArticleDOI
Transmuted kumaraswamy distribution
TL;DR: In this article, the authors proposed a generalization of the Kumaraswamy distribution referred to as the transmuted Kumarashwamy (𝑇𝐾𝑤) distribution, which is developed using the quadratic rank transmutation map studied by Shaw et al.
Journal ArticleDOI
Performance rating of the transmuted exponential distribution: an analytical approach
TL;DR: The so called Transmuted Exponential (TE) distribution was applied to two real life datasets to assess its potential flexibility over some other generalized models.
References
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Journal ArticleDOI
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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