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A NEW FAMILY OF GENERALIZED GAMMA DISTRIBUTION AND ITS APPLICATION | Science Publications

机译:广义伽玛分布的一个新族及其应用科学出版物

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> The mixture distribution is defined as one of the most important ways to obtain new probability distributions in applied probability and several research areas. According to the previous reason, we have been looking for more flexible alternative to the lifetime data. Therefore, we introduced a new mixed distribution, namely the Mixture Generalized Gamma (MGG) distribution, which is obtained by mixing between generalized gamma distribution and length biased generalized gamma distribution is introduced. The MGG distribution is capable of modeling bathtub-shaped hazard rate, which contains special sub-models, namely, the exponential, length biased exponential, generalized gamma, length biased gamma and length biased generalized gamma distributions. We present some useful properties of the MGG distribution such as mean, variance, skewness, kurtosis and hazard rate. Parameter estimations are also implemented using maximum likelihood method. The application of the MGG distribution is illustrated by real data set. The results demonstrate that MGG distribution can provide the fitted values more consistent and flexible framework than a number of distribution include important lifetime data; the generalized gamma, length biased generalized gamma and the three parameters Weibull distributions.
机译: >混合分布被定义为在应用概率和一些研究领域中获取新概率分布的最重要方法之一。根据先前的原因,我们一直在寻找寿命数据的更灵活替代方案。因此,我们引入了一种新的混合分布,即混合广义伽玛(MGG)分布,它是通过广义伽玛分布和长度有偏差的广义伽玛分布之间的混合而获得的。 MGG分布能够对浴缸状的危险率进行建模,其中包含特殊的子模型,即指数,长度偏向指数,广义伽马,长度偏向伽马和长度偏向广义伽马分布。我们介绍了MGG分布的一些有用属性,例如均值,方差,偏度,峰度和危险率。还使用最大似然法来实现参数估计。 MGG分布的应用通过实际数据集进行说明。结果表明,与大量重要寿命数据相比,MGG分布可以提供更一致,更灵活的拟合值框架;广义伽玛,长度偏差广义伽玛和三个参数威布尔分布。

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