Locally D-optimal designs for non-linear models on the k-dimensional ball

被引：2

作者：

Radloff, Martin ^{[1
]}

Schwabe, Rainer ^{[1
]}

机构：

[1] Otto von Guericke Univ, Inst Math Stochast, PF 4120, D-39016 Magdeburg, Germany

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2019年 / 203卷

关键词：

Censored data; Generalized linear models; k-dimensional ball; Multiple regression models; Negative binomial regression; Poisson regression; REGRESSION;

D O I：

10.1016/j.jspi.2019.03.004

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper we construct (locally) D-optimal designs for a wide class of non-linear multiple regression models, when the design region is a k-dimensional ball. For this construction we make use of the concept of invariance and equivariance in the context of optimal designs. As examples we consider Poisson and negative binomial regression as well as proportional hazard models with censoring. By generalization we can extend these results to arbitrary ellipsoids as design regions. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：106 / 116

页数：11

共 16 条

[1]

[Anonymous], 1974, Measure Theory

[2]

[Anonymous], 1972, Theory of optimal experiments

[3] Elemental information matrices and optimal experimental design for generalized regression models [J].