Comparison of strategies when building linear prediction models

SUMMARYIn statistical and biometric sciences, one often uses predictive linear models. The initial form of such models is usually obtained by fitting the coefficients of the model to a set of observed data according to the classical least squares method. Newborn models that are obtained in this way...

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Bibliographic Details
Published in:Numerical linear algebra with applications 2014-10, Vol.21 (5), p.618-628
Main Authors: Pestman, Wiebe R., Groenwold, Rolf H.H., Teerenstra, Steven
Format: Article
Language:English
Subjects:
Biometrics
building strategy
Buildings
Construction
Least squares method
linear
Linear prediction
Mathematical models
predictive model
Raw
Strategy
validation
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Summary:SUMMARYIn statistical and biometric sciences, one often uses predictive linear models. The initial form of such models is usually obtained by fitting the coefficients of the model to a set of observed data according to the classical least squares method. Newborn models that are obtained in this way will be referred to as raw models. Such raw models are often subject of efforts to improve them as to their predictive performance on external datasets. Several methods can be followed to fine‐tune raw models, thus leading to a variety of model building strategies. In this paper, the idea of so‐called victory rates is introduced to compare the performance of building strategies mutually.Copyright © 2013 John Wiley & Sons, Ltd.
ISSN:1070-5325
1099-1506
DOI:10.1002/nla.1916