Comparison of designs for generalized linear models under model misspecification
DSpace at IIT Bombay
View Archive InfoField | Value | |
Title |
Comparison of designs for generalized linear models under model misspecification
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Creator |
MUKHOPADHYAY, S
KHURI, AI |
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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 |
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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.
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Publisher |
ELSEVIER SCIENCE BV
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Date |
2014-10-15T15:07:39Z
2014-10-15T15:07:39Z 2012 |
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Type |
Article
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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 |
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Language |
en
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