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, a new family of distributions called Kumaraswamy-generalized power Weibull (Kgpw) distribution is proposed and studied, which has a number of well known sub-models such as Weibbull, exponentiated Weibell, Kumar aswamy Weibler, generalized power Weibrler, and new sub-model, namely, generalized generalized power exponential distributions.
Abstract: A new family of distributions called Kumaraswamy-generalized power Weibull (Kgpw) distribution is proposed and studied. This family has a number of well known sub-models such as Weibull, exponentiated Weibull, Kumaraswamy Weibull, generalized power Weibull and new sub-models, namely, exponentiated generalized power Weibull, Kumaraswamy generalized power exponential distributions. Some statistical properties of the new distribution include its moments, moment generating function, quantile function and hazard function are derived. In addition, maximum likelihood estimates of the model parameters are obtained. An application as well as comparisons of the Kgpw and its sub-distributions is given. Keywords: Generalized power Weibull distribution, Kumaraswamy distribution, Maximum likelihood estimation, Moment generating function, Hazard rate function.
13 citations
TL;DR: In this paper, a new count model which is obtained by compounding negative binomial distribution with Kumaraswamy distribution has been introduced, which has several properties such as it can be nested to different compound distributions on specific parameter setting.
Abstract: The present paper introduces a new count model which is obtained by compounding negative binomial distribution with Kumaraswamy distribution. The proposed model has several properties such as it can be nested to different compound distributions on specific parameter setting. Similarity of the proposed model with existing compound distribution has been shown by means of reparameterization. Factorial moments and parameter estimation through maximum likelihood estimation and method of moment have been disused. The potentiality of the proposed model has been tested by chi-square goodness of fit test by modeling the real world count data sets from genetics and ecology.
13 citations
TL;DR: Inflation at one of the extremes of the standard unit interval and also the more challenging case in which inflation takes place at both interval endpoints are considered.
Abstract: The Kumaraswamy distribution is useful for modeling variables whose support is the standard unit interval, i.e., (0, 1). It is not uncommon, however, for the data to contain zeros and/or ones. When that happens, the interest shifts to modeling variables that assume values in [0, 1), (0, 1] or [0, 1]. Our goal in this paper is to introduce inflated Kumaraswamy distributions that can be used to that end. We consider inflation at one of the extremes of the standard unit interval and also the more challenging case in which inflation takes place at both interval endpoints. We introduce inflated Kumaraswamy distributions, discuss their main properties, show how to estimate their parameters (point and interval estimation) and explain how testing inferences can be performed. We also present Monte Carlo evidence on the finite sample performances of point estimation, confidence intervals and hypothesis tests. An empirical application is presented and discussed.
12 citations
TL;DR: In this article, the authors introduced and studied exponentiated generalized Kumaraswamy distribution and derived mathematical properties including quantile function, moment generating function, ordinary moments, probability weighted moments, incomplete moments, and Renyi entropy.
Abstract: In this article, we introduced and studied exponentiated generalized Kumaraswamy distribution. We derived mathematical properties including quantile function, moment generating function, ordinary moments, probability weighted moments, incomplete moments, and Renyi entropy. The expressions of order statistics are also derived. Here we discuss the parameter estimation by using the method of maximum likelihood. We showed resilience of the introduced distribution over existing some well-known distributions by using real dataset applications.
12 citations
TL;DR: In this paper, a five-parameter lifetime distribution called the McDonald extended exponential model was proposed to generalize the exponential, generalized exponential, Kumaraswamy exponential and beta exponential distributions, among others.
Abstract: We study a five-parameter lifetime distribution called the McDonald extended exponential model to generalize the exponential, generalized exponential, Kumaraswamy exponential and beta exponential distributions, among others. We obtain explicit expressions for the moments and incomplete moments, quantile and generating functions, mean deviations, Bonferroni and Lorenz curves and Gini concentration index. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. The applicability of the new model is illustrated by means of a real data set.
11 citations