|
Robert St Aubin
ABSTRACT
Automatic smoothing parameter selection methods for nonparametric
regression like cross-validation and generalized cross-validation are
known to be severely affected by dependence in the regression errors.
We proposed, in this work, to modify some of the ideas used in the
cross-validation criterion in kernel regression with dependent errors
and apply them to
smoothing splines with model based penalty. Model based penalty
smoothing permits us to keep the flexibility of the nonparametric
methods while it also allows us to
specify a favoured parametric model which can help improve on the
estimate of the regression function.
We consider the ``modified cross-validation'' (also known as
Leave-2l+1 out) and the ``blockwise cross-validation'' smoothing
parameter selection techniques which were initially proposed by
Wehrly and Hart (1988) and Hardle and Vieu (1992) respectively.
These two smoothing parameter selection techniques take correlation
into account and alleviate its effect on the regression function estimation.
We use a simulation study to evaluate the performance of our two
smoothing parameter selection techniques. We compare the results with
a few commonly used parametric techniques. Our techniques are also
applied to an air pollution data set where we estimate the
underlying trend of daily and monthly ground ozone levels in southern Ontario.
Chapter 0: Abstract, table of contents, list of tables
and figures.
|
|
|