Original title: Regression for High-Dimensional Data: From Regularization to Deep Learning
Authors: Kalina, Jan ; Vidnerová, Petra
Document type: Papers
Conference/Event: International Days of Statistics and Economics /14./, Prague (CZ), 20200910
Year: 2020
Language: eng
Abstract: 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.
Keywords: high-dimensional data; neural networks; regression; regularization; robustness
Project no.: GA19-05704S (CEP), GA18-23827S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: The 14th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-22-3

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2020/article/252-Kalina-Jan-paper.pdf
Original record: http://hdl.handle.net/11104/0313657

Permalink: http://www.nusl.cz/ntk/nusl-432440


The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers