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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A New Weighted Half-Logistic Distribution:Properties, Applications and Different Method of Estimations
Majid Hashempour,Morad Alizadeh +1 more
TL;DR: In this article , a new two-parameter lifetime distribution based on arctan function which is called weighted half-logistic (WHL) distribution was introduced and the theoretical properties of this model including quantile function, extreme value, linear combination for pdf and cdf, moments, conditional moments, moment generating function and mean deviation are derived and studied in details.
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On Properties of Length-Biased Exponential Model
TL;DR: In this article , the type II half logistic length-biased exponential distribution (TIIHLLBE) model is introduced and examined, and statistical features such as moments, conditional moments, mean order statistics, and entropy are derived.
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Poisson Transmuted-G Family of Distributions: Its Properties and Applications
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A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications.
Gauss M. Cordeiro,Julio Cezar Souza Vasconcelos,Edwin M. M. Ortega,Pedro Rafael Diniz Marinho +3 more
TL;DR: In this paper , the authors define two new flexible families of continuous distributions to fit real data by compounding the Marshall-Olkin class and the power series distribution, which are very competitive to the popular beta and Kumaraswamy generators.
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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