Publicaties
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Model Selection Within the Class of Discrete-Time Markovian Models Universiteit Gent
Model selection and model averaging KU Leuven
Correction for Model Selection Bias Using a Modified Model Averaging Approach for Supervised Learning Methods Applied to EEG Experiments Universiteit Hasselt KU Leuven
This paper proposes a modified model averaging approach for linear discriminant analysis. This approach is used in combination with a doubly hierarchical supervised learning analysis and applied to preclinical pharmaco-electroencephalographical data for classification of psychotropic drugs. Classification of a test dataset was highly improved with this method.
A qualitative model structure sensitivity analysis method to support model selection Universiteit Gent
Model selection and model averaging KU Leuven
Model selection methods provide a way to select one model amongst a set of models in a statistically valid way. Such methods include tools for variable selection in regression models. Asymptotic properties such as consistency and efficiency, the specific use of the model, or properties regarding minimization of a certain risk function such as the expected prediction error, may help to decide which method to choose. Model selection is a special ...