Statistical Modelling 11 (2011), 237252
Variable selection in additive models by non-negative garrote
Eva Cantoni
Institute of Statistics and Department of Economics,
University of Geneva
CH1211 Geneva
Switzerland
eMail: eva.cantoni@unige.ch
Joanna Mills Flemming
Department of Mathematics and Statistics,
Dalhousie University
Canada
Elvezio Ronchetti
Institute of Statistics and Department of Economics,
University of Geneva
Switzerland
Abstract:
We adapt Breiman’s non-negative garrote method to perform variable
selection in non-parametric additive models. The technique avoids
methods of testing for which no general reliable distributional
theory is available. In addition, it removes the need for a full
search of all possible models, something which is computationally
intensive, especially when the number of variables is moderate to
high. The method has the advantages of being conceptually simple
and computationally fast. It provides accurate predictions and is
effective at identifying the variables generating the model. To
illustrate our procedure, we analyse logbook data on blue sharks
(Prionace glauca) from the US pelagic longline fishery. In addition,
we compare our proposal to a series of available alternatives by
simulation. The results show that in all cases our methods perform
better or as well as these alternatives.
Keywords:
Blue shark logbook data; cross-validation; non-negative garrote;
non-parametric regression; shrinkage methods; variable selection
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