The Azzalini Skew-t Information Matrix Evaluation and Use for Standard Error Calculations

Chindhanai Uthaisaad, Doug Martin
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Abstract

The Azzalini skew-t distributions are popular because of their theoretical foundation and the availability of computational methods in the R package sn. One difficulty with this skew-t family is that the elements of the expected information matrix do not have closed form analytic formulas. Thus, we developed a numerical integration method of computing the expected information matrix in the R package skewtInfo. The accuracy of our expected information matrix calculation method was confirmed by comparing the result with that obtained using an observed information matrix for a very large sample size. A Monte Carlo study to evaluate the accuracy of the finite-sample standard errors obtained with our expected information matrix calculation method, for the case of three realistic skew-t parameter vectors, indicates that use of the expected information matrix results in standard errors as accurate as, and sometimes a little more accurate than, use of an observed information matrix.
Azzalini歪斜信息矩阵的评估及其在标准误差计算中的应用
Azzalini偏t分布由于其理论基础和R包sn中计算方法的可用性而广受欢迎。这种斜t族的一个困难是期望信息矩阵的元素没有封闭形式的解析公式。因此,我们开发了一种计算R包skewtInfo中期望信息矩阵的数值积分方法。通过将所期望的信息矩阵计算方法与使用观测到的信息矩阵计算结果在非常大的样本量下进行比较,证实了该方法的准确性。一项蒙特卡罗研究评估了用我们的期望信息矩阵计算方法得到的有限样本标准误差的准确性,对于三个实际的偏t参数向量,表明使用期望信息矩阵得到的标准误差与使用观测信息矩阵一样准确,有时甚至比使用观测信息矩阵更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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