Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31053
Authors: 
Krause, Rüdiger
Tutz, Gerhard
Year of Publication: 
2004
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 389
Abstract: 
In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use separate toolsto solve the different tasks of variable selection and smoothing parameter choice. The combinationof these tools often leads to inappropriate results. In this paper we propose a simulataneous choice of variables and smoothing parameters based on genetic algorithms. Common genetic algorithms have to be modified since inclusion of variables and smoothing have to be coded separately but are linked in the search for optimal solutions. The basic tool for fitting the additive model is the penalized expansion in B-splines.
Subjects: 
Genetic algorithm
Additive model
Variable selection
Penalized regression splines
B-splines
Improved AIC
BIC
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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