On kernel-based nonlinear regression estimation

0551774 - ÚI 2022 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Vidnerová, Petra
On kernel-based nonlinear regression estimation.
The 15th International Days of Statistics and Economics Conference Proceedings. Slaný: Melandrium, 2021 - (Löster, T.; Pavelka, T.), s. 450-459. ISBN 978-80-87990-25-4.
[International Days of Statistics and Economics /15./. Prague (CZ), 09.09.2021-11.09.2021]
Grant CEP: GA ČR GA21-05325S
Institucionální podpora: RVO:67985807
Klíčová slova: nonlinear regression * machine learning * kernel smoothing * regularization * regularization networks
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://msed.vse.cz/msed_2021/sbornik/toc.html

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
Trvalý link: http://hdl.handle.net/11104/0326994