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 paper, Kumaraswamy distribution with non-Bayes and Bayes estimators were used to estimate the unknown shape parameter, and the maximum likelihood was obtained as a non-parametric estimator.
Abstract: This paper is considered with Kumaraswamy distribution. Numerical, non-Bayes and Bayes methods of estimation were used to estimate the unknown shape parameter. The maximum likelihood is obtained as a non-Bayes estimator. As well as, Bayes estimators under a symmetric loss function (De-groot and NLINEX) by using four types of informative priors three double priors and one single prior. In addition, numerical estimators are obtained by using Newton's method and the false position method. Simulation research is conducted for the comparison of the effectiveness of the proposed estimators. Matlab 2015 will be used to obtain the numerical results.
3 citations
TL;DR: This article introduces a new generalization of the skew-t distribution based on the Kumaraswamy generalized distribution, which has the ability of fitting skewed, long, and heavy-tailed data and is more flexible than the skewness distribution as it contains the skew -t distribution as a special case.
Abstract: Skew normal distribution is an alternative distribution to the normal distribution to accommodate asymmetry. Since then extensive studies have been done on applying Azzalini’s skewness mechanism to...
3 citations
TL;DR: In this article, the shape parameters, reliability and hazard rate functions of the inverted Kumaraswamy distribution are estimated using maximum likelihood and Bayesian methods based on dual generalized order statistics.
Abstract: In this paper, the shape parameters, reliability and hazard rate functions of the inverted Kumaraswamy distribution are estimated using maximum likelihood and Bayesian methods based on dual generalized order statistics. The Bayes estimators are derived under the squared error loss function as a symmetric loss function and the linear-exponential loss function as an asymmetric loss function based on dual generalized order statistics. Confidence and credible intervals for the parameters, reliability and hazard rate functions are obtained. All results are specialized to lower record values, also a numerical study is presented to illustrate the theoretical procedures.
3 citations
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TL;DR: In this article, a generic relationship between the distributions established by a simple transformation between arbitrary Kumaraswamy-distributed random variables and certain beta-distribution random variables is established by means of the logistic map, the paradigmatic example of a discrete nonlinear dynamical system.
Abstract: The Kumaraswamy distribution has been proposed as an alternative to the beta distribution with more benign algebraic properties. They have the same two parameters, the same support and qualitatively similar shape for any parameter values. There is a generic relationship between the distributions established by a simple transformation between arbitrary Kumaraswamy-distributed random variables and certain beta-distributed random variables. Here, a different relationship is established by means of the logistic map, the paradigmatic example of a discrete non-linear dynamical system.
3 citations
TL;DR: In this paper, the problem of statistical inference of constant-stress ALTs based on censored data is discussed in a life testing experiments, accelerated life tests (ALTs) model has provided a significant decrease for the cost and time.
Abstract: In a life testing experiments, accelerated life tests (ALTs) model has provided a significant decrease for the cost and time. The problem of statistical inference of constant-stress ALTs based on censored data is discussed in this paper. So, we implement partially constant-stress ALTs model to test units have two parameter Kumaraswamy lifetime population under adaptive Type-II progressive censoring scheme. The population parameters as well as acceleration factor are estimated by using maximum likelihood method for point and interval estimation. Two different confidence intervals are obtained under bootstrap technique. Also, Bayesian approach under different loss functions is used to contract the point and interval estimates of the model parameters with the help of Markov chain Monte Carlo method (MCMC). For illustrative purpose a simulate data set are analyzed. Different developed results discussed in this paper are compared through Monte Carlo simulation study.
2 citations