论文标题
在Tweedie和几何Tweedie模型之间区分（半）连续类
Discriminating between and within (semi)continuous classes of both Tweedie and geometric Tweedie models
论文作者
论文摘要
在Tweedie和几何Tweedie型号中，通用功率参数$ p \ Notin（0,1）$作为自动分配选择。它主要分离出两个半连续的子类（$ 1 <p <2 $）和正面连续（$ p \ geq 2 $）。我们的论文集中在基于最大似然比测试和最小kolmogorov-smirnov距离方法探索诊断工具的中心，以根据$ p $的值区分这两个模型的每个子类中的每个子类中的非常紧密的分布。以独特的变化指数为基础，我们还分别区分了伽玛和几何伽玛分布，分别是$ p = 2 $的Tweedie和几何Tweedie家族。通过模拟检查了分散参数，均值和样本量多种组合的正确选择的概率。因此，我们进行了数值比较研究，以评估两个家庭的这些子类中的歧视程序。最后，从广义上讲，半连续（$ 1 <p \ leq 2 $）发行版比过多变化的连续（$ p> 2 $）更有区别；并研究了两个用于说明目的的数据集。
In both Tweedie and geometric Tweedie models, the common power parameter $p\notin(0,1)$ works as an automatic distribution selection. It mainly separates two subclasses of semicontinuous ($1<p<2$) and positive continuous ($p\geq 2$) distributions. Our paper centers around exploring diagnostic tools based on the maximum likelihood ratio test and minimum Kolmogorov-Smirnov distance methods in order to discriminate very close distributions within each subclass of these two models according to values of $p$. Grounded on the unique equality of variation indices, we also discriminate the gamma and geometric gamma distributions with $p=2$ in Tweedie and geometric Tweedie families, respectively. Probabilities of correct selection for several combinations of dispersion parameters, means and sample sizes are examined by simulations. We thus perform a numerical comparison study to assess the discrimination procedures in these subclasses of two families. Finally, semicontinuous ($1<p\leq 2$) distributions in the broad sense are significantly more distinguishable than the over-varied continuous ($p>2$) ones; and two datasets for illustration purposes are investigated.
