Title: Robust model selection using fast and robust bootstrap
Authors: Salibian-Barrera, Matlas ×
Van Aelst, Stefan #
Issue Date: 2008
Series Title: COMPUTATIONAL STATISTICS & DATA ANALYSIS vol:52 issue:12 pages:5121-5135
Abstract: Robust model selection procedures control the undue influence that outliers can
have on the selection criteria by using both robust point estimators and a bounded
loss function when measuring either the goodness-of-fit or the
expected prediction
error of each model. Furthermore, to avoid favoring over-fitting models, these two
measures can be combined with a penalty term for the size of the model. The expected prediction error conditional on the observed data ma
y be estimated using
the bootstrap. However, bootstrapping robust estimators becomes extremely time
consuming on moderate to high dimensional data sets. It is sh
own that the expected
prediction error can be estimated using a very fast and robust bootstrap method,
and that this approach yields a consistent model selection method that is computationally feasible even for a relatively large number of co
variates. Moreover, as
opposed to other bootstrap methods, this proposal avoids the numerical problems
associated with the small bootstrap samples required to obtain consistent model selection criteria. The finite-sample performance of the fas
t and robust bootstrap
model selection method is investigated through a simulation study while its feasi-
bility and good performance on moderately large regression
models are illustrated
on several real data examples.
ISSN: 0167-9473
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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