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.

The Kumaraswamy-transmuted exponentiated modified Weibull distribution

Abstract: This article introduces a new generalization of the transmuted exponentiated modified Weibull distribution introduced by Eltehiwy and Ashour in 2013, using Kumaraswamy distribution introduced by Cordeiro and de Castro in 2011. We refer to the new distribution as Kumaraswamy-transmuted exponentiated modified Weibull (Kw-TEMW) distribution. The new model contains 54 lifetime distributions as special cases such as the KumaraswamyWeibull, exponentiated modified Weibull, exponentiated Weibull, exponentiated exponential, transmuted Weibull, Rayleigh, linear failure rate, and exponential distributions, among others. The properties of the new model are discussed and the maximum likelihood estimation is used to evaluate the parameters. Explicit expressions are derived for the moments and examine the order statistics. This model is capable of modeling various shapes of aging and failure criteria.

Consumer Welfare and GM Food Labeling: A Simulation Using an Adjusted Kumaraswamy Distribution

Abstract: Genetically modified (GM) food products and their labeling have become a major policy issue with impassioned public debates. We explore the impact of different labeling regimes on consumer attitudes towards GM products and consumer welfare. Our experimental results illustrate that these consumer attitudes do not follow the Uniform distribution as has often been assumed in the literature but instead fit an adjusted Kumaraswamy distribution. If a Uniform distribution is assumed, the advantage of mandatory labeling would be exaggerated. Using an adjusted Kumaraswamy distribution our simulation results demonstrate that voluntary labeling is superior to mandatory labeling with the higher separation cost, while mandatory labeling is not necessarily better with lower separation cost. Therefore, the governments of China and other countries with similar consumer characteristics should consider voluntary labeling for GM food while encouraging innovations that reduce the price of GM food as well as controlling the opportunistic behavior of its producers so as to enhance the advantage of voluntary labeling.

Estimation and prediction of the Kumaraswamy distribution based on record values and inter-record times

Abstract: The maximum likelihood and Bayesian approaches for estimating the parameters and the prediction of future record values for the Kumaraswamy distribution has been considered when the lower record values along with the number of observations following the record values (inter-record-times) have been observed. The Bayes estimates are obtained based on a joint bivariate prior for the shape parameters. In this case, Bayes estimates of the parameters have been developed by using Lindley's approximation and the Markov Chain Monte Carlo (MCMC) method due to the lack of explicit forms under the squared error and the linear-exponential loss functions. The MCMC method has been also used to construct the highest posterior density credible intervals. The Bayes and the maximum likelihood estimates are compared by using the estimated risk through Monte Carlo simulations. We further consider the non-Bayesian and Bayesian prediction for future lower record values arising from the Kumaraswamy distribution based on ...

On the Kumaraswamy Kumaraswamy distribution

Abstract: In this paper, new distribution so called the Kumaraswamy – Kumaraswamy (KW-KW) distribution, as a Special model from the class of Kumaraswamy Generalized (KW-G) distributions, is introduced the probability density function (pdf), the cumulative distribution function (cdf), moments, quantiles, the median, the mode, the mean deviation, the entropy, order statistics, L-moments and parameters estimation based on maximum likelihood are obtained A numerical illustration is used to studying the properties of the parameters

Double bounded Kumaraswamy-power series class of distributions

Abstract: In this paper, we will introduce the new Kumaraswamy-power series class of distributions. This new class is obtained by compounding the Kumaraswamy distribution of Kumaraswamy (1980) and the family of power series distributions. The new class contains some new double bounded distributions such as the Kumaraswamy-geometric, -Poisson, -logarithmic and -binomial, which are used widely in hydrology and related areas. In addition, the corresponding hazard rate function of the new class can be increasing, decreasing, bathtub and upside-down bathtub. Some basic properties of this class of distributions such as the moment generating function, moments and order statistics are studied. Some special members of the class are also investigated in detail. The maximum likelihood method is used for estimating the unknown parameters of the members of the new class. Finally, an application of the proposed class is illustrated using a real data set.