Detailed Information on Publication Record
2017
Local bandwidth selectors for functional kernel regression
KURUCZOVÁ, Daniela and Jan KOLÁČEKBasic information
Original name
Local bandwidth selectors for functional kernel regression
Authors
KURUCZOVÁ, Daniela and Jan KOLÁČEK
Edition
2017
Other information
Language
English
Type of outcome
Konferenční abstrakt
Field of Study
10101 Pure mathematics
Country of publisher
Spain
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Organization unit
Faculty of Science
Keywords in English
Functional data; nonparametric regression; kernel methods; bandwidth selection
Tags
International impact, Reviewed
Změněno: 12/1/2018 14:50, doc. Mgr. Jan Koláček, Ph.D.
Abstract
V originále
Nonparametric regression methods based on kernel smoothing rely on selection of suitable bandwidth parameter in order to minimize the mean squared error. We focus on the functional regression, i.e. the case when the predictor is a functional random variable. In finite-dimensional setting, global selection of bandwidth (same bandwidth is used for all data points) is often sufficient. Due to sparsity of infinite dimensional space, local bandwidth selection (different bandwidth is chosen for each data point) seems to be more suitable approach. In our study, we focus on local bandwidth selection methods. Furthermore, we propose local bandwidth selection method based on penalizing functions. Using simulation studies, we compare local bandwidth selection methods with each other and with their global counterparts.
Links
| GA15-06991S, research and development project |
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