Other formats:
BibTeX
LaTeX
RIS
@article{1390756,
author = {Kuruczová, Daniela and Koláček, Jan},
article_location = {Prague},
article_number = {3},
keywords = {Functional data; nonparametric regression; kernel methods; bandwidth selection},
language = {eng},
issn = {0322-788X},
journal = {Statistika: Statistics and Economy Journal},
title = {Bandwidth Selection Problem in Nonparametric Functional Regression},
url = {https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0},
volume = {97},
year = {2017}
}
TY - JOUR
ID - 1390756
AU - Kuruczová, Daniela - Koláček, Jan
PY - 2017
TI - Bandwidth Selection Problem in Nonparametric Functional Regression
JF - Statistika: Statistics and Economy Journal
VL - 97
IS - 3
SP - 107-115
EP - 107-115
PB - Czech Statistical Office
SN - 0322788X
KW - Functional data
KW - nonparametric regression
KW - kernel methods
KW - bandwidth selection
UR - https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0
L2 - https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0
N2 - The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems in functional kernel regression are choosing an optimal smoothing parameter and selecting an appropriate semimetric as a distance measure. The former is the focus of this paper – several data-driven methods for optimal bandwidth selection are described and discussed. The performance of these methods is illustrated in a real data application. A conclusion is drawn that local bandwidth selection methods are more appropriate in the functional setting.
ER -
KURUCZOVÁ, Daniela and Jan KOLÁČEK. Bandwidth Selection Problem in Nonparametric Functional Regression. \textit{Statistika: Statistics and Economy Journal}. Prague: Czech Statistical Office, 2017, vol.~97, No~3, p.~107-115. ISSN~0322-788X.
|
