近日，文理学院岳荣先教授与其合作者在统计学领域重要国际期刊Statistics 发表了题为 Optimal designs for linear regression models with skew-normal errors 的学术论文，内容摘要如下：

This paper investigates optimal designs for linear regression models with skew-normal errors which provide an alternative to traditional models with symmetric normal errors and eliminate the need for data transformation. Using the maximum likelihood estimation approach, we consider various design criteria, including D-, A-, and As-optimality. Equivalence theorems are provided to determine the optimality of the designs. Optimal designs for a centered parametrization model are also considered. Two examples are provided to illustrate the theoretical results. In addition, the efficiencies of the optimal designs based on skew-normal errors are compared with those based on normal errors.

全文链接为 https://doi.org/10.1080/02331888.2024.2410802