On robust cross-validation for nonparametric smoothing
Loading...
Date
2010-05-12T07:16:49Z
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Procedures for local-constant smoothing are investigated in a broad
variety of data situations with outliers and jumps. Moving window
and nearest neighbour versions of mean and median smoothers are
considered, as well as double window and linear hybrid smoothers.
For the choice of the window width or the number of neighbours the
different estimators are combined with each of several cross-validation
criteria like least squares, least absolute deviations, and median-cross-validation. It is identified, which method works best in which data
scenarios. Although there is not a single overall best robust smoothing procedure, a robust cross-validation criterion, called least trimmed
squares-cross-validation, gives good results for most smoothing methods and data situations, with cross-validation based on least absolute
deviations being a strong competitor, particularly if there are jumps,
but little problems with outliers in the data.
Description
Table of contents
Keywords
Jump, Least trimmed squares-cross-validation, Local-constant smoothing, LTS-CV, Outlier, Robust smoothing procedure