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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

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • e-ISSN

  • 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