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Comparison of designs for generalized linear models under model misspecification

DSpace at IIT Bombay

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Title Comparison of designs for generalized linear models under model misspecification
 
Creator MUKHOPADHYAY, S
KHURI, AI
 
Subject Kriging
Linear predictor
Mean-squared error of prediction
Model bias
Response surface methodology
RESPONSE-SURFACE DESIGNS
MEAN SQUARED ERROR
REGRESSION-MODELS
ROBUST DESIGNS
PREDICTION
BIAS
SIMULATION
CRITERION
 
Description The purpose of this article is to demonstrate the use of the quantile dispersion graphs (QDGs) approach for comparing candidate designs for generalized linear models in the presence of model misspecification in the linear predictor. The proposed design criterion is based on the mean-squared error of prediction which incorporates the prediction variance and the bias caused by fitting the wrong model. The method of kriging is used to estimate the unknown function assumed to be the cause of model misspecification. The QDGs approach is also useful in assessing the robustness of a given design to values of the unknown parameters in the linear predictor. Three numerical examples are presented to illustrate the application of the proposed methodology. (C) 2011 Elsevier B.V. All rights reserved.
 
Publisher ELSEVIER SCIENCE BV
 
Date 2014-10-15T15:07:39Z
2014-10-15T15:07:39Z
2012
 
Type Article
 
Identifier STATISTICAL METHODOLOGY, 9(3)285-304
1572-3127
1878-0954
http://dx.doi.org/10.1016/j.stamet.2011.08.004
http://dspace.library.iitb.ac.in/jspui/handle/100/15118
 
Language en