Maximum likelihood method for bandwidth selection in kernel conditional density estimate
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F19%3APU134070" target="_blank" >RIV/00216305:26110/19:PU134070 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14310/19:00111007
Result on the web
<a href="https://link.springer.com/article/10.1007/s00180-019-00884-0" target="_blank" >https://link.springer.com/article/10.1007/s00180-019-00884-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00180-019-00884-0" target="_blank" >10.1007/s00180-019-00884-0</a>
Alternative languages
Result language
angličtina
Original language name
Maximum likelihood method for bandwidth selection in kernel conditional density estimate
Original language description
This paper discusses the kernel estimator of conditional density. A significant problem of kernel smoothing is bandwidth selection. The problem consists in the fact that optimal bandwidth depends on the unknown conditional and marginal density. This is the reason why some data-driven method needs to be applied. In this paper, we suggest a method for bandwidth selection based on a classical maximum likelihood approach. We consider a slight modification of the original method—the maximum likelihood method with one observation being left out. Applied to two types of conditional density estimators—to the Nadaraya–Watson and local linear estimator, the proposed method is compared with other known methods in a simulation study. Our aim is to compare the methods from different points of view, concentrating on the accuracy of the estimated bandwidths, on the final model quality measure, and on the computational time.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTATIONAL STATISTICS & DATA ANALYSIS
ISSN
0943-4062
e-ISSN
1613-9658
Volume of the periodical
34
Issue of the periodical within the volume
4
Country of publishing house
DE - GERMANY
Number of pages
16
Pages from-to
1871-1887
UT code for WoS article
000501848900019
EID of the result in the Scopus database
2-s2.0-85064158959