Kernel Estimation of the Regression Function - Bandwidth Selection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F02%3A00021250" target="_blank" >RIV/00216224:14310/02:00021250 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Kernel Estimation of the Regression Function - Bandwidth Selection
Original language description
The problem of deciding how much to smooth is of great importance in nonparametric regression. Before embarking on technical solutions of the problem it is worth noting that a selection of the smoothing parameter is always related to a certain interpretation of the smooth. However, a good automatically selected parameter is always a useful starting (view)point. An advantage of automatic selection of the bandwidth for kernel smoothers is that comparison between laboratories can be made on the basis of astandardized method. Various methods for choosing the smoothing parameter are presented in the following sections. The choice is made so that some global error criterion is minimized. This paper shortly aspires to summarize attained results from this branch and to demonstrate their application for simulated data sets.
Czech name
Jádrové odhady regresní funkce - volba optimální šířky okna
Czech description
Práce se zabývá některými metodami pro výběr optimální šířky okna při neparametrické regresi.

Publication year
2002
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Datastat 01, Folia Fac. Sci. Nat. Univ. Masaryk. Brunensis, Mathematica 11
ISBN
80-210-3028-3
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
129-138
Publisher name
Masaryk University
Place of publication
Brno
Event location
Orlické mountains, Čihák Cottage
Event date
Jan 1, 2001
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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