| KOLÁČEK, Jan and Ivanka HOROVÁ. Bandwidth matrix selectors for kernel regression. Computational Statistics. HEIDELBERG, GERMANY: SPRINGER HEIDELBERG, 2017, vol. 32, No 3, p. 1027-1046. ISSN 0943-4062. Available from: https://dx.doi.org/10.1007/s00180-017-0709-3. |
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@article{1319858,
author = {Koláček, Jan and Horová, Ivanka},
article_location = {HEIDELBERG, GERMANY},
article_number = {3},
doi = {http://dx.doi.org/10.1007/s00180-017-0709-3},
keywords = {multivariate kernel regression; constrained bandwidth matrix; kernel smoothing; mean integrated square error},
language = {eng},
issn = {0943-4062},
journal = {Computational Statistics},
title = {Bandwidth matrix selectors for kernel regression},
url = {http://is.muni.cz/auth/repo/1319858/template_cost.pdf},
volume = {32},
year = {2017}
}
TY - JOUR
ID - 1319858
AU - Koláček, Jan - Horová, Ivanka
PY - 2017
TI - Bandwidth matrix selectors for kernel regression
JF - Computational Statistics
VL - 32
IS - 3
SP - 1027-1046
EP - 1027-1046
PB - SPRINGER HEIDELBERG
SN - 09434062
KW - multivariate kernel regression
KW - constrained bandwidth matrix
KW - kernel smoothing
KW - mean integrated square error
UR - http://is.muni.cz/auth/repo/1319858/template_cost.pdf
L2 - http://is.muni.cz/auth/repo/1319858/template_cost.pdf
N2 - 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.
ER -
KOLÁČEK, Jan and Ivanka HOROVÁ. Bandwidth matrix selectors for kernel regression. \textit{Computational Statistics}. HEIDELBERG, GERMANY: SPRINGER HEIDELBERG, 2017, vol.~32, No~3, p.~1027-1046. ISSN~0943-4062. Available from: https://dx.doi.org/10.1007/s00180-017-0709-3.
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