具有下界约束混料试验的Monte-Carlo渐近最优设计  

Monte-Carlo Asymptotic Optimal Design in Mixture Experiment with Lower Bound Constraints

作　　者：杨晓珍[1] Yang Xiaozhen(School of Science,Kaili University,Kaili,Guizhou 556011)

机构地区：[1]凯里学院理学院,贵州凯里556011

出　　处：《绥化学院学报》2023年第2期142-147,共6页Journal of Suihua University

基　　金：国家自然科学基金(11901260);贵州省科学技术基金(黔科合基础[2020]1Y010)

摘　　要：在具有下界约束的混料试验设计中,由于难以求得含有未知变量的信息矩阵的行列式的显式表达,所以难以得到各类最优准则下的精确最优设计解。文章使用Monte-Carlo方法构造随机试验点的样本,进而可以通过模拟得出D-准则和MV-准则下的渐近最优设计。In the mixture design of experiment with lower bound constraints.Because it is difficult to obtain explicit expression of the information matrix’s determinant which containing the unknown variables,it hard to get the accurate optimal design solution under various optimal criterion.In this paper,we using the Monte-Carlo method to structure random testing samples,then can obtain the asymptotic optimal design by simulation under D-criterion and MV-criterion.

关 键 词：MONTE-CARLO方法 D-准则 MV-准则 信息矩阵 

分 类 号：O212.6[理学—概率论与数理统计]