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On the goodness-of-fit tests for gamma generalized linear models
Journal of the Korean Statistical Society ( IF 0.6 ) Pub Date : 2021-01-03 , DOI: 10.1007/s42952-020-00095-0
Seongil Jo , Myeongjee Lee , Woojoo Lee

An omitted covariate in the regression function leads to hidden or unobserved heterogeneity in generalized linear models (GLMs). Using this fact, we develop two novel goodness-of-fit tests for gamma GLMs. The first is a score test to check the existence of hidden heterogeneity and the second is a Hausman-type specification test to detect the difference between two estimators for the dispersion parameter. In addition to these developments, we reveal the undesirable behavior of the deviance test for gamma GLMs, which is still used by many scholars in practice. Exploiting real-world data, we demonstrate the application of our proposed method.



中文翻译:

关于伽玛广义线性模型的拟合优度检验

回归函数中省略的协变量会导致广义线性模型(GLM)中隐藏或未观察到的异质性。利用这一事实,我们开发了两种新颖的伽玛GLM拟合优度测试。第一个是评分测试,用于检查是否存在隐藏的异质性,第二个是Hausman型规范测试,用于检测两个估计值之间的色散参数之间的差异。除了这些发展,我们还揭示了伽马GLM偏差测试的不良行为,许多学者仍在实践中使用它。利用真实世界的数据,我们演示了我们提出的方法的应用。

更新日期:2021-01-04
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