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New Families of Distributions for Modeling Bivariate Data, with Applications

机译:用于建模双变量数据的新分布族及其应用

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In this study we introduce a new method of adding two shape parameters to any baseline bivariate distribution function (df) to get a more flexible family of bivariate df's. Through the additional parameters we can fully control the type of the resulting family. This method is applied to yield a new two-parameter extension of the bivariate standard normal distribution, denoted by BSSN. The statistical properties of the BSSN family are studied. Moreover, via a mixture of the BSSN family and the standard bivariate logistic df, we get a more capable family, denoted by FBSSN. Theoretically, each of the marginals of the FBSSN contains all the possible types of df's with respect to the signs of skewness and excess kurtosis. In addition, each possesses very wide range of the indices of skewness and kurtosis. Finally, we compare the families BSSN and FBSSN with some important competitors (i.e., some generalized families of bivariate df's) via real data examples. AMS 2010 Subject Classification: 62-07; 62E10; 62F99.
机译:在这项研究中,我们介绍了一种将两个形状参数添加到任何基线双变量分布函数(df)以获得更灵活的双变量df族的新方法。通过附加参数,我们可以完全控制生成的族的类型。该方法适用于产生由BSSN表示的双变量标准正态分布的新的两参数扩展。研究了BSSN系列的统计特性。此外,通过BSSN家族和标准双变量logistic df的混合,我们得到了功能更强大的家族,用FBSSN表示。从理论上讲,就偏斜和峰度过大的迹象而言,FBSSN的每个边缘都包含所有可能的df类型。另外,每个都具有非常广泛的偏度和峰度指数。最后,我们通过实际数据示例将BSSN和FBSSN家族与一些重要的竞争对手(即一些双变量df的广义家族)进行了比较。 AMS 2010主题分类:62-07; 62E10; 62F99。

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