Publication details

Bandwidth matrix selectors for kernel regression

Authors

KOLÁČEK Jan HOROVÁ Ivanka

Year of publication 2017
Type Article in Periodical
Magazine / Source Computational Statistics
MU Faculty or unit

Faculty of Science

Citation
Web http://is.muni.cz/auth/repo/1319858/template_cost.pdf
Doi http://dx.doi.org/10.1007/s00180-017-0709-3
Field General mathematics
Keywords multivariate kernel regression; constrained bandwidth matrix; kernel smoothing; mean integrated square error
Attached files
Description Choosing a bandwidth matrix belongs to the class of significant problems in multivariate kernel regression. The problem consists of the fact that a theoretical optimal bandwidth matrix depends on the unknown regression function which to be estimated. Thus data-driven methods should be applied. A method proposed here is based on a relation between asymptotic integrated square bias and asymptotic integrated variance. Statistical properties of this method are also treated. The last two sections are devoted to simulations and an application to real data.
Related projects: