Publication details

Bandwidth matrix selectors for multivariate kernel regression



Year of publication 2014
Type Conference abstract
MU Faculty or unit

Faculty of Science

Description The most important factor in multivariate kernel regression is a choice of a bandwidth matrix. This choice is particularly important, because of its role in controlling both the amount and the direction of multivariate smoothing. Considerable attention has been paid to constrained parameterization of the bandwidth matrix such as a diagonal matrix. The proposed method is based on an optimally balanced relation between the integrated variance and the integrated squared bias. The utility of the method is illustrated through a simulation study and real data applications.

You are running an old browser version. We recommend updating your browser to its latest version.

More info