Název: Regression for High-Dimensional Data: From Regularization to Deep Learning
Autoři: Kalina, Jan ; Vidnerová, Petra
Typ dokumentu: Příspěvky z konference
Konference/Akce: International Days of Statistics and Economics /14./, Prague (CZ), 20200910
Rok: 2020
Jazyk: eng
Abstrakt: Regression modeling is well known as a fundamental task in current econometrics. However, classical estimation tools for the linear regression model are not applicable to highdimensional data. Although there is not an agreement about a formal definition of high dimensional data, usually these are understood either as data with the number of variables p exceeding (possibly largely) the number of observations n, or as data with a large p in the order of (at least) thousands. In both situations, which appear in various field including econometrics, the analysis of the data is difficult due to the so-called curse of dimensionality (cf. Kalina (2013) for discussion). Compared to linear regression, nonlinear regression modeling with an unknown shape of the relationship of the response on the regressors requires even more intricate methods.
Klíčová slova: high-dimensional data; neural networks; regression; regularization; robustness
Číslo projektu: GA19-05704S (CEP), GA18-23827S (CEP)
Poskytovatel projektu: GA ČR, GA ČR
Zdrojový dokument: The 14th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-22-3

Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: https://msed.vse.cz/msed_2020/article/252-Kalina-Jan-paper.pdf
Původní záznam: http://hdl.handle.net/11104/0313657

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-432440


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