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  • 标题:Skew-t Expected Information Matrix Evaluation and Use for Standard Error Calculations
  • 本地全文:下载
  • 作者:R. Douglas Martin ; Chindhanai Uthaisaad ; Daniel Z. Xia
  • 期刊名称:R News
  • 印刷版ISSN:1609-3631
  • 出版年度:2020
  • 卷号:12
  • 期号:1
  • 页码:188-205
  • 语种:English
  • 出版社:The R Foundation for Statistical Computing
  • 摘要:Skew-t distributions derived from skew-normal distributions, as developed by Azzalini and several co-workers, 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 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.
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