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, a modification of L-moments method to make it suitable for censored data directly (namely, Direct Lmoments) is proposed, which concentrates on Type-I censored data.
Abstract: This paper suggests a modification of L-moments method to make it suitable for censored data directly (namely: Direct L-moments). This study concentrates on Type-I censored data. The modification i...
2 citations
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TL;DR: In this paper, a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process is proposed, which exhibits symmetry under the same conditions as the well-known Dirichlet.
Abstract: We construct a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process. We explore and develop the theoretical properties of this new distribution and prove that it exhibits symmetry under the same conditions as the well-known Dirichlet. Like the Dirichlet, the new distribution is adept at capturing sparsity but, unlike the Dirichlet, has an exact and closed form reparameterization--making it well suited for deep variational Bayesian modeling. We demonstrate the distribution's utility in a variety of semi-supervised auto-encoding tasks. In all cases, the resulting models achieve competitive performance commensurate with their simplicity, use of explicit probability models, and abstinence from adversarial training.
2 citations
Posted Content•
TL;DR: A new manner of improving neural networks by introducing a bunch of copies of the same neuron modeled by the generalized Kumaraswamy distribution is investigated, and a novel non-linear activation function is proposed which is closely related to ReLU.
Abstract: Deep neural networks have recently achieved state-of-the-art results in many machine learning problems, e.g., speech recognition or object recognition. Hitherto, work on rectified linear units (ReLU) provides empirical and theoretical evidence on performance increase of neural networks comparing to typically used sigmoid activation function. In this paper, we investigate a new manner of improving neural networks by introducing a bunch of copies of the same neuron modeled by the generalized Kumaraswamy distribution. As a result, we propose novel non-linear activation function which we refer to as Kumaraswamy unit which is closely related to ReLU. In the experimental study with MNIST image corpora we evaluate the Kumaraswamy unit applied to single-layer (shallow) neural network and report a significant drop in test classification error and test cross-entropy in comparison to sigmoid unit, ReLU and Noisy ReLU.
2 citations
Journal Article•
TL;DR: In this article, a new distribution called the Odd Generalized Exponential Kumaraswamy (OGE-K) distribution is defined and its properties investigated, which includes its probability density function, moment, moment generating function, characteristic function, quantile function, reliability analysis and order statistics.
Abstract: In this study, a new distribution called the Odd Generalized Exponential Kumaraswamy (OGE-K) distribution is defined and its properties investigated. The properties of the new distribution verified includes its probability density function, moment, moment generating function, characteristic function, quantile function, reliability analysis and order statistics. The maximum likelihood estimation procedure is used to estimate the parameters of the new distribution. Application of real data set indicates that the proposed distribution would serve as a good alternative to Kumaraswamy distribution among others to model real- life data in many areas.
2 citations
TL;DR: In this article, a new four-parameter distribution is introduced, and moments, conditional moments, and moment generating function of the new distribution including are presented, and the estimation of its parameters are studied.
Abstract: In this paper, a new four-parameter distribution is introduced. Moments, conditional moments and moment generating function of the new distribution including are presented. Estimation of its parameters are studied. Two real data applications are described to show its superior performance versus some known lifetime models.
2 citations