Efficient approximate leave-one-out cross-validation for ridge and lasso

Master thesis (2010)

Authors

Contributors

J.J. Goeman (mentor)
M.J.T. Reinders (mentor)
Programme
Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2010 Meijer, R.J.
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Published Date

09-11-2010

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Programme

Bioinformatics

Abstract

In this thesis an approximation method is discussed that provides similar results to leave-one-out cross-validation but is less time-consuming. By means of this approximation method, estimating the optimal values of ridge and lasso parameters will take less time and carrying out (an approximated version of) double LOOCV will become practically feasible. The method can be used in generalized linear models as well as in Cox' proportional hazards model. In order to show its usefulness, the method is tested on several microarray data sets.

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