Bivariate Gamma Random Vectors
提出一个七参数双变量概率分布族，允许任意伽马边际分布、正负相关及多种回归曲线，便于参数估计和计算机模拟，并讨论了其在一阶自回归时间序列中的应用。
A seven-parameter family of bivariate probability distributions is developed which allows for any gamma marginal distributions, any associated correlation (positive or negative), and a range of regression curves. The form of the family, which relies on the reproducibility property of the gamma distribution, is motivated by the search for tractable parameter estimation, general dependency structure, and straightforward computer sampling for simulation modeling. A modification with closed-form parameter estimation, but less general dependency structure, is also given. Finally, the use of these distributions in the form of first order autoregressive time series is discussed.