Print Email Facebook Twitter Efficient approximate leave-one-out cross-validation for ridge and lasso Title Efficient approximate leave-one-out cross-validation for ridge and lasso Author Meijer, R.J. Contributor Goeman, J.J. (mentor) Reinders, M.J.T. (mentor) Faculty Electrical Engineering, Mathematics and Computer Science Department Media and Knowledge Engineering Programme Bioinformatics Date 2010-11-09 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. Subject cross-validationridgelassoapproximation method To reference this document use: http://resolver.tudelft.nl/uuid:d9b5456d-722a-401d-9f1a-c530c46d6491 Part of collection Student theses Document type master thesis Rights (c) 2010 Meijer, R.J. Files PDF scriptie.pdf 966.69 KB Close viewer /islandora/object/uuid:d9b5456d-722a-401d-9f1a-c530c46d6491/datastream/OBJ/view