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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Properties of the Transmuted Burr XII Distribution, Regression and its Applications
TL;DR: A log-linear model is formulated and developed using the new distribution so-called the log-transmuted Burr XII distribution for modeling data with a unimodal failure rate function, as an alternative to thelog-McDonald Burr XII, log-beta Burr XII), log-Kumaraswamy Burr XII and logistic regression models.
Journal ArticleDOI
A new distribution function with bounded support: the reflected Generalized Topp-Leone Power Series distribution
Francesca Condino,Filippo Domma +1 more
TL;DR: In this article, a new flexible class of distributions with bounded support, called reflected Generalized Topp-Leone Power Series (rGTL-PS), was introduced by compounding the reflected generalized ToppLeone (van Drop and Kotz, 2006) and the family of Power Series distributions.
Journal ArticleDOI
On Making an Informed Choice between Two Lomax-based Continuous Probability Distributions Using Lifetime Data
Terna Godfrey Ieren,S. O. Oyamakin,Abubakar Yahaya,Angela Chukwu,Adamu Abubakar Umar,Samson Kuje +5 more
TL;DR: In this paper, the Lomax-Weibull and LOMax-Log-Logistic distributions were applied to some selected datasets to compare their performance and provide useful insight on how to select the most fit among them when dealing with a real-life situation.
Journal ArticleDOI
On the Estimation of Parameters of Kumaraswamy-G Distributions
TL;DR: In this paper, Cheng and Amin and Ranneby introduced a new method of estimating parameters based on Kullback-Leibler divergence (the maximum spacing (MSP) method).
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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