Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/xmlui/handle/100/18114
Title: Robust Designs in Generalized Linear Models: AQuantile Dispersion Graphs Approach
Authors: DAS, I
AGGARWAL, M
MUKHOPADHYAY, S
Keywords: Regression-Models
Logistic-Models
Transformation
Prediction
Families
Issue Date: 2015
Publisher: TAYLOR & FRANCIS INC
Citation: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 44(9SI)2348-2370
Abstract: 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.
URI: http://dx.doi.org/10.1080/03610918.2014.904343
http://dspace.library.iitb.ac.in/jspui/handle/100/18114
ISSN: 0361-0918
1532-4141
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