| HOROVÁ, Ivanka, Jan KOLÁČEK and Kamila VOPATOVÁ. Visualization and Bandwidth Matrix Choice. Communications in Statistics - Theory and Methods. Philadelphia: Taylor & Francis, 2012, vol. 41, No 4, p. 759-777. ISSN 0361-0926. Available from: https://dx.doi.org/10.1080/03610926.2010.529539. |
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@article{947253,
author = {Horová, Ivanka and Koláček, Jan and Vopatová, Kamila},
article_location = {Philadelphia},
article_number = {4},
doi = {http://dx.doi.org/10.1080/03610926.2010.529539},
keywords = {product kernel; bandwidth matrix; mean integrated square error; asymptotic mean integrated square error},
language = {eng},
issn = {0361-0926},
journal = {Communications in Statistics - Theory and Methods},
title = {Visualization and Bandwidth Matrix Choice},
volume = {41},
year = {2012}
}
TY - JOUR
ID - 947253
AU - Horová, Ivanka - Koláček, Jan - Vopatová, Kamila
PY - 2012
TI - Visualization and Bandwidth Matrix Choice
JF - Communications in Statistics - Theory and Methods
VL - 41
IS - 4
SP - 759-777
EP - 759-777
PB - Taylor & Francis
SN - 03610926
KW - product kernel
KW - bandwidth matrix
KW - mean integrated square error
KW - asymptotic mean integrated square error
N2 - Kernel smoothers are among the most popular nonparametric functional estimates. These estimates depend on a bandwidth which controls the smoothness of the estimate. While the literature for a bandwidth choice in a univariate density estimate is quite extensive, the progress in the multivariate case is slower. We focus on a bandwidth matrix selection for a bivariate kernel density estimate provided that the bandwidth matrix is diagonal. A common task is to find entries of the bandwidth matrix which minimizes the Mean Integrated Square Error (MISE). It is known that in this case there exists explicit solution of an asymptotic approximation of MISE (Wand and Jones, 1995). In the present paper we pay attention to the visualization and optimizers are presented as intersection of bivariate functional surfaces derived from this explicit solution and we develop the method based on this visualization. A simulation study compares the least square cross-validation method and the proposed method. Theoretical results are applied to real data.
ER -
HOROVÁ, Ivanka, Jan KOLÁČEK and Kamila VOPATOVÁ. Visualization and Bandwidth Matrix Choice. \textit{Communications in Statistics - Theory and Methods}. Philadelphia: Taylor \&{} Francis, 2012, vol.~41, No~4, p.~759-777. ISSN~0361-0926. Available from: https://dx.doi.org/10.1080/03610926.2010.529539.
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