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Robust Designs in Generalized Linear Models: AQuantile Dispersion Graphs Approach

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Title Robust Designs in Generalized Linear Models: AQuantile Dispersion Graphs Approach
 
Creator DAS, I
AGGARWAL, M
MUKHOPADHYAY, S
 
Subject REGRESSION-MODELS
LOGISTIC-MODELS
TRANSFORMATION
PREDICTION
FAMILIES
Family of link functions
Kriging
Logistic link
Parameter orthogonality
Standardization
 
Description 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.
 
Publisher TAYLOR & FRANCIS INC
 
Date 2016-01-15T08:01:10Z
2016-01-15T08:01:10Z
2015
 
Type Article
 
Identifier COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 44(9SI)2348-2370
0361-0918
1532-4141
http://dx.doi.org/10.1080/03610918.2014.904343
http://dspace.library.iitb.ac.in/jspui/handle/100/18114
 
Language en