Original title: On kernel-based nonlinear regression estimation
Authors: Kalina, Jan ; Vidnerová, Petra
Document type: Papers
Conference/Event: International Days of Statistics and Economics /15./, Prague (CZ), 20210909
Year: 2021
Language: eng
Abstract: 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
Keywords: kernel smoothing; machine learning; nonlinear regression; regularization; regularization networks
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: The 15th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-25-4
Note: Související webová stránka: https://msed.vse.cz/msed_2021/sbornik/toc.html

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0326994

Permalink: http://www.nusl.cz/ntk/nusl-508421


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