Robust Designs in Generalized Linear Models: AQuantile Dispersion Graphs Approach

被引：1
|
作者：
Das, I. [1 ]
Aggarwal, M. [2 ]
Mukhopadhyay, S. [3 ]
机构：
[1] Kumaun Univ, Dept Stat, Almora, India
[2] Univ Memphis, Dept Math Sci, Memphis, TN 38152 USA
[3] Indian Inst Technol, Dept Math, Bombay, Maharashtra, India
来源：
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2015年 / 44卷 / 09期
关键词：
Family of link functions; Kriging; Logistic link; Parameter orthogonality; Standardization; REGRESSION-MODELS; LOGISTIC-MODELS; TRANSFORMATION; PREDICTION; FAMILIES;
D O I：
10.1080/03610918.2014.904343
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：
This article studies design selection for generalized linear models (GLMs) using the quantile dispersion graphs (QDGs) approach in the presence of misspecification in the link and/or linear predictor. The uncertainty in the linear predictor is represented by a unknown function and estimated using kriging. For addressing misspecified link functions, a generalized family of link functions is used. Numerical examples are shown to illustrate the proposed methodology.
引用
收藏
页码：2348 / 2370
页数：23
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