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Robustness of orthogonal uniform composite designs against missing data
Communications in Statistics - Theory and Methods ( IF 0.8 ) Pub Date : 2021-06-16 , DOI: 10.1080/03610926.2021.1927095
Brenda Mbouamba Yankam 1 , Abimibola Victoria Oladugba 1
Affiliation  

Abstract

Missing data can hardly be prevented in most experiments due to setbacks that occur during the data entry process, and may grossly affect the estimation of the regression coefficients. The effects of missing data may be extremely significant if the design is almost saturated, saturated or fully saturated. It is of practical significance to identify designs that are least affected by missing data. In this paper, new orthogonal uniform minimax loss (OUCM) designs are constructed. These designs are more robust to one missing design point than the original orthogonal uniform composite designs (OUCDs) of Zhang, Liu, and Zhou (2020 Zhang, X., M. Liu, and Y. Zhou. 2020. Orthogonal uniform composite designs. Journal of Statistical Planning and Inference 206:10010. doi:10.1016/j.jspi.2019.08.007.[Crossref], [Web of Science ®] , [Google Scholar]). The proposed designs are compared with central composite design (CCDs), small composite designs (SCDs), orthogonal array composite designs (OACDs), orthogonal array composite minimax loss designs (OACMs), and orthogonal uniform composite designs (OUCDs) based on the D-efficiency, A-efficiency and T-efficiency for estimating the parameters of the second-order model and under the generalized scaled standard deviations. These designs perform better in term of losses and also have higher D-efficiency, A-efficiency and T-efficiency.



中文翻译:

正交均匀复合设计对缺失数据的稳健性

摘要

由于在数据输入过程中发生的挫折,在大多数实验中很难避免丢失数据,并且可能严重影响回归系数的估计。如果设计几乎饱和、饱和或完全饱和,缺失数据的影响可能会非常显着。确定受缺失数据影响最小的设计具有实际意义。在本文中,构建了新的正交均匀极小极大损失 (OUCM) 设计。与 Zhang、Liu 和 Zhou 的原始正交均匀复合设计 (OUCD) ( 2020 ) Zhang, X. , M. LiuY. Zhou2020 年正交均匀复合设计统计规划与推理杂志206:10010。doi: 10.1016/j.jspi.2019.08.007[交叉引用]、[Web of Science®]  、[谷歌学术搜索])。将所提出的设计与中心复合设计 (CCD)、小型复合设计 (SCD)、正交阵列复合设计 (OACD) 、正交阵列复合极小极大损失设计 (OACM)) 进行比较,基于D -efficiency、A -efficiency 和T -efficiency,用于估计二阶模型的参数和广义尺度标准差。这些设计在损耗方面表现更好,并且还具有更高的D效率、A效率和T效率。

更新日期:2021-06-16
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