Maximum Entropy Empirical Likelihood Methods Based on Bivariate Laplace Transforms and Moment Generating Functions
Maximum Entropy Empirical Likelihood Methods Based on Bivariate Laplace Transforms and Moment Generating Functions作者机构：école d’Actuariat Université Laval Québec Canada
出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)
年 卷 期：2018年第8卷第2期
页 面：264-283页
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Bivariate Power Mixture Operator Laplace Transform Copulas Chi-Square Test Statistics Bivariate Normality Infinite Divisibility Empirical Likelihood
摘 要：Maximum Entropy Empirical Likelihood (MEEL) methods are extended to bivariate distributions with closed form expressions for their bivariate Laplace transforms (BLT) or moment generating functions (BMGF) without closed form expressions for their bivariate density functions which make the implementation of the likelihood methods difficult. These distributions are often encountered in joint modeling in actuarial science and finance. Moment conditions to implement MEEL methods are given and a bivariate Laplace transform power mixture (BLTPM) is also introduced, the new operator generalizes the existing univariate one in the literature. Many new bivariate distributions including infinitely divisible(ID) distributions with closed form expressions for their BLT can be created using this operator and MEEL methods can also be applied to these bivariate distributions.
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