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Summarizing the predictive power of a generalized linear model

Summarizing the predictive power of a generalized linear model This paper studies summary measures of the predictive power of a generalized linear model, paying special attention to a generalization of the multiple correlation coefficient from ordinary linear regression. The population value is the correlation between the response and its conditional expectation given the predictors, and the sample value is the correlation between the observed response and the model predicted value. We compare four estimators of the measure in terms of bias, mean squared error and behaviour in the presence of overparameterization. The sample estimator and a jack‐knife estimator usually behave adequately, but a cross‐validation estimator has a large negative bias with large mean squared error. One can use bootstrap methods to construct confidence intervals for the population value of the correlation measure and to estimate the degree to which a model selection procedure may provide an overly optimistic measure of the actual predictive power. Copyright © 2000 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

Summarizing the predictive power of a generalized linear model

Zheng, Beiyao; Agresti, Alan
Statistics in Medicine , Volume 19 (13) – Jul 15, 2000
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References (26)

Publisher
Wiley
Copyright
Copyright © 2000 John Wiley & Sons, Ltd.
ISSN
0277-6715
eISSN
1097-0258
DOI
10.1002/1097-0258(20000715)19:13<1771::AID-SIM485>3.0.CO;2-P
Publisher site
See Article on Publisher Site

Abstract

This paper studies summary measures of the predictive power of a generalized linear model, paying special attention to a generalization of the multiple correlation coefficient from ordinary linear regression. The population value is the correlation between the response and its conditional expectation given the predictors, and the sample value is the correlation between the observed response and the model predicted value. We compare four estimators of the measure in terms of bias, mean squared error and behaviour in the presence of overparameterization. The sample estimator and a jack‐knife estimator usually behave adequately, but a cross‐validation estimator has a large negative bias with large mean squared error. One can use bootstrap methods to construct confidence intervals for the population value of the correlation measure and to estimate the degree to which a model selection procedure may provide an overly optimistic measure of the actual predictive power. Copyright © 2000 John Wiley & Sons, Ltd.

Journal

Statistics in Medicine – Wiley

Published: Jul 15, 2000

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