论文标题
单变量拟合优点测试的综合经验比较。
A comprehensive empirical power comparison of univariate goodness-of-fit tests for the Laplace distribution
论文作者
论文摘要
在本文中，我们介绍了40个单变量拉普拉斯分布的经验功率比较的结果，该测试使用带有样本量的蒙特卡洛模拟$ n = 20、50、100、200 $，显着性水平$α= 0.01、0.01、0.05、0.10 $，以及400个备用案例和对称性较重的较重/对称性的较重/替代品。除了我们研究的无与伦比的范围外，有趣的贡献是为选择替代方案的创新设计提出的建议。 400个替代方案由20个特定案例组成，共有20个subpodels，这些子模型来自主要的11个模型。对于每个子模型，20个特定情况对应于选择覆盖全功率范围的参数值。对结果的分析导致建议对替代分布的五个不同分组进行最佳测试。还提出了一个真实的示例，其中在最近的四年期间，适用于每周的亚马逊股票的单变量拉普拉斯分布的适当测试。
In this paper we present the results from an empirical power comparison of 40 goodness-of-fit tests for the univariate Laplace distribution, carried out using Monte Carlo simulations with sample sizes $n = 20, 50, 100, 200$, significance levels $α= 0.01, 0.05, 0.10$, and 400 alternatives consisting of asymmetric and symmetric light/heavy-tailed distributions taken as special cases from 11 models. In addition to the unmatched scope of our study, an interesting contribution is the proposal of an innovative design for the selection of alternatives. The 400 alternatives consist of 20 specific cases of 20 submodels drawn from the main 11 models. For each submodel, the 20 specific cases corresponded to parameter values chosen to cover the full power range. An analysis of the results leads to a recommendation of the best tests for five different groupings of the alternative distributions. A real-data example is also presented, where an appropriate test for the goodness-of-fit of the univariate Laplace distribution is applied to weekly log-returns of Amazon stock over a recent four-year period.
