Topic
Kumaraswamy distribution
About: Kumaraswamy distribution is a research topic. Over the lifetime, 213 publications have been published within this topic receiving 3393 citations. The topic is also known as: Kumaraswamy's double bounded distribution.
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TL;DR: In this article, the reliability of the stress-strength model was discussed and the reliability functions were obtained for a component which has an independent strength and is exposed to two and three stresses, respectively.
Abstract: This paper discusses reliability of the stress-strength model The reliability functions ð‘…1 and ð‘…2 were obtained for a component which has an independent strength and is exposed to two and three stresses, respectively We used the generalized inverted Kumaraswamy distribution GIKD with unknown shape parameter as well as known shape and scale parameters The parameters were estimated from the stress- strength models, while the reliabilities ð‘…1, ð‘…2 were estimated by three methods, namely the Maximum Likelihood, Least Square, and Regression
A numerical simulation study a comparison between the three estimators by mean square error is performed It is found that best estimator between the three estimators is Maximum likelihood estimators
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TL;DR: In this paper, a new generalization of the Topp-Leone distribution with a unit interval is defined and studied, namely Mixed ToppLeone-Kumaraswamy distribution.
Abstract: In this article, a new generalization of the Topp-Leone distribution with a unit interval, namely Mixed Topp-Leone-Kumaraswamy distribution is defined and studied The mathematical properties of this mixing distribution are described Moments, quantile function, R?nyi entropy, incomplete moments and moments of residual are obtained for the new Mixed Topp-Leone - Kumaraswamy distribution The maximum likelihood (MLE), Crans (CM) , Percentile (PM) and Particle Swarm Optimization(PSO) estimators of the parameters are derived The percentile Method is more efficient method as compred to the others Two real data sets are used to illustrate an application and superiority of the proposed distribution
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TL;DR: In this article, the kumaraswamy reciprocal family of distributions is introduced as a new continues model with some of approximation to other probabilistic models as reciprocal, beta, uniform, power function, exponential, negative exponential, weibull, rayleigh and pareto distribution.
Abstract: In this paper, kumaraswamy reciprocal family of distributions is introduced as a new continues model with some of approximation to other probabilistic models as reciprocal, beta, uniform, power function, exponential, negative exponential, weibull, rayleigh and pareto distribution. Some fundamental distributional properties, force of mortality, mills ratio, bowley skewness, moors kurtosis, reversed hazard function, integrated hazard function, mean residual life, probability weighted moments, bonferroni and lorenz curves, laplace-stieltjes transform of this new distribution with the maximum likelihood method of the parameter estimation are studied. Finally, four real data sets originally presented are used to illustrate the proposed estimators.