On kernel-based nonlinear regression estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00551774" target="_blank" >RIV/67985807:_____/21:00551774 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/21:00555825
Result on the web
<a href="https://msed.vse.cz/msed_2021/sbornik/toc.html" target="_blank" >https://msed.vse.cz/msed_2021/sbornik/toc.html</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
On kernel-based nonlinear regression estimation
Original language description
This paper is devoted to two important kernel-based tools of nonlinear regression: the Nadaraya-Watson estimator, which can be characterized as a successful statistical method in various econometric applications, and regularization networks, which represent machine learning tools very rarely used in econometric modeling. This paper recalls both approaches and describes their common features as well as differences. For the Nadaraya-Watson estimator, we explain its connection to the conditional expectation of the response variable. Our main contribution is numerical analysis of suitable data with an economic motivation and a comparison of the two nonlinear regression tools. Our computations reveal some tools for the Nadaraya-Watson in R software to be unreliable, others not prepared for a routine usage. On the other hand, the regression modeling by means of regularization networks is much simpler and also turns out to be more reliable in our examples. These also bring unique evidence revealing the need for a careful choice of the parameters of regularization networks
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA21-05325S" target="_blank" >GA21-05325S: Modern nonparametric methods in econometrics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 15th International Days of Statistics and Economics Conference Proceedings
ISBN
978-80-87990-25-4
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
450-459
Publisher name
Melandrium
Place of publication
Slaný
Event location
Prague
Event date
Sep 9, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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