Fossaluza Victor, Esteves Luís Gustavo, Pereira Carlos Alberto de Bragança
Institute of Mathematics and Statistics, Universidade de São Paulo, São Paulo, SP 05508-090, Brazil.
Entropy (Basel). 2018 Mar 14;20(3):194. doi: 10.3390/e20030194.
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.
测量随机变量之间的相关性是统计学中最基本的问题之一，因此，确定相关变量的联合分布至关重要。Copulas函数最近已成为正确推断感兴趣变量联合分布的重要工具。尽管许多研究都涉及连续变量的情况，但很少有研究专注于处理离散变量。本文提出了一种使用Copulas函数和伯恩斯坦多项式来估计具有有界支撑的联合离散分布的非参数方法。我们展示了在真实强迫症数据中的应用。
