We introduce a new class of regression models based on the geometric Tweedie models (GTMs) for analyzing both continuous and semicontinuous data, similar to the recent and standard Tweedie regression models. We also present a phenomenon of variation with respect to the equi-varied exponential distribution, where variance is equal to the squared mean. The corresponding power v-functions which characterize the GTMs, obtained in turn by exponential-Tweedie mixture, are transformed into variance to use the conventional generalized linear models. The real power parameter of GTMs works as an automatic distribution selection such for asymmetric Laplace, geometric-compound-Poisson-gamma and geometric-Mittag-Leffler. The classification of all power v-functions reveals only two border count distributions, namely geometric and geometric-Poisson. We establish practical properties, into the GTMs, of zero-mass and variation phenomena, also in connection with some reliability measures. Simulation studies show that the proposed model highlights asymptotic unbiased and consistent estimators, despite the general over-variation. We illustrate two applications, under- and over-varied, on real datasets to a time to failure and time to repair in reliability; one of which has positive values with many achievements in zeros. We finally make concluding remarks, including future directions.

中文翻译：

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具有变化现象的连续和半连续数据的几何Tweedie回归模型

我们引入了基于几何Tweedie模型（GTM）的一类新的回归模型，用于分析连续和半连续数据，类似于最新的Tweedie回归模型和标准Tweedie回归模型。我们还提出了一种关于等变指数分布的变化现象，其中方差等于均方根。依次通过指数特威迪混合获得的表征GTM的相应幂函数，将其转化为方差以使用常规的广义线性模型。GTM的有功功率参数可以用作自动分配选择，例如不对称拉普拉斯，几何复合泊松伽玛和几何Mittag-Leffler。所有幂v函数的分类仅显示两个边界计数分布，即几何和几何泊松。我们还结合一些可靠性措施，在GTM中建立了零质量和变化现象的实用属性。仿真研究表明，尽管存在一般的过度变化，但所提出的模型仍突出了渐近无偏和一致的估计量。我们将实际数据集上的两种应用（低估和高估）应用于故障发生时间和可靠性修复时间。其中之一具有正值，并且在零方面取得了许多成就。我们最后作总结性发言，包括未来的方向。在实际数据集上低估或高估了故障发生时间和可靠性修复时间；其中之一具有正值，并且在零方面取得了许多成就。我们最后作总结性发言，包括未来的方向。在实际数据集上低估或高估了故障发生时间和可靠性修复时间；其中之一具有正值，并且在零方面取得了许多成就。我们最后作总结性发言，包括未来的方向。