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首页> 外文期刊>Allgemeines statistisches archiv >Absolute continuous bivariate generalized exponential distribution
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Absolute continuous bivariate generalized exponential distribution

机译：绝对连续双变量广义指数分布

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摘要

Generalized exponential distribution has been used quite effectively to model positively skewed lifetime data as an alternative to the well known Weibull or gamma distributions. In this paper we introduce an absolute continuous bivariate generalized exponential distribution by using a simple transformation from a well known bivariate exchangeable distribution. The marginal distributions of the proposed bivariate generalized exponential distributions are generalized exponential distributions. The joint probability density function and the joint cumulative distribution function can be expressed in closed forms. It is observed that the proposed bivariate distribution can be obtained using Clayton copula with generalized exponential distribution as marginals. We derive different properties of this new distribution. It is a five-parameter distribution, and the maximum likelihood estimators of the unknown parameters cannot be obtained in closed forms. We propose some alternative estimators, which can be obtained quite easily, and they can be used as initial guesses to compute the maximum likelihood estimates. One data set has been analyzed for illustrative purposes. Finally we propose some generalization of the proposed model.
机译：广义指数分布已非常有效地用于建模正偏寿命数据，以替代众所周知的威布尔或伽马分布。在本文中，我们通过使用众所周知的双变量可交换分布的简单转换来介绍绝对连续双变量广义指数分布。提出的二元广义指数分布的边际分布是广义指数分布。联合概率密度函数和联合累积分布函数可以用封闭形式表示。可以看出，可以使用具有广义指数分布作为边际的Clayton copula来获得建议的二元分布。我们得出此新分布的不同属性。它是一个五参数分布，并且无法以封闭形式获得未知参数的最大似然估计。我们提出了一些替代估计量，这些估计量可以很容易地获得，并且可以用作初始猜测来计算最大似然估计。为了说明目的，已经分析了一个数据集。最后，我们对所提出的模型进行一些概括。

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