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Estimating Multivariate Discrete Distributions Using Bernstein Copulas

Institute of Mathematics and Statistics, Universidade de São Paulo, São Paulo, SP 05508-090, Brazil
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This paper is an extended version of our conference paper presented at the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), Jarinu/SP, Brazil, 9–14 July 2017.
Entropy 2018, 20(3), 194; https://doi.org/10.3390/e20030194
Received: 18 December 2017 / Revised: 1 March 2018 / Accepted: 7 March 2018 / Published: 14 March 2018
Measuring the dependence between random variables is one of the most fundamental problems in statistics, and therefore, determining the joint distribution of the relevant variables is crucial. Copulas have recently become an important tool for properly inferring the joint distribution of the variables of interest. Although many studies have addressed the case of continuous variables, few studies have focused on treating discrete variables. This paper presents a nonparametric approach to the estimation of joint discrete distributions with bounded support using copulas and Bernstein polynomials. We present an application in real obsessive-compulsive disorder data. View Full-Text
Keywords: Bernstein polynomial; copula; nonparametric inference; Aitchison’s distance Bernstein polynomial; copula; nonparametric inference; Aitchison’s distance
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MDPI and ACS Style

Fossaluza, V.; Esteves, L.G.; Pereira, C.A.B. Estimating Multivariate Discrete Distributions Using Bernstein Copulas. Entropy 2018, 20, 194.

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