K.B. Kulasekera
Department of Mathematical Sciences
Clemson University
Variable Selection in Nonparametric Regression
We consider variable selection issue in a nonparametric regression setting.
Two procedures based on variance estimators and a procedure based on testing
the equality of regression curves are proposed for selecting the significant
variables in a general nonparametric regression model. These procedures do
not require multidimensional smoothing at intermediate steps and they are
based on formal tests of hypotheses as opposed to existing methods in the
literature. Large sample properties
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