Publication details

Use of Fourier transformation for kernel smoothing



Year of publication 2004
Type Article in Proceedings
Conference Proceedings in Computational Statistics COMPSTAT'04
MU Faculty or unit

Faculty of Science

Field General mathematics
Keywords Bandwidth selection; nonparametric regression;Fourier transform; kernel estimation
Description The problem of bandwidth selection for non-parametric kernel regression is considered. We will follow the Nadaraya - Watson and local linear estimators especially. The circular design is assumed in this work to avoid the difficulties caused by boundary effects. Most of bandwidth selectors are based on the residual sum of squares (RSS). It is often observed in simulation studies that these selectors are biased toward undersmoothing. This leads to consideration of a procedure which stabilizes the RSS by modifying the periodogram of the observations. Simulation studies suggest that the proposed selector is much more consistent than the classical one.
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