Regression for High-Dimensional Data: From Regularization to Deep Learning

0535704 - ÚI 2021 RIV CZ eng C - Conference Paper (international conference)
Kalina, Jan - Vidnerová, Petra
Regression for High-Dimensional Data: From Regularization to Deep Learning.
The 14th International Days of Statistics and Economics Conference Proceedings. Slaný: Melandrium, 2020 - (Löster, T.; Pavelka, T.), s. 418-427. ISBN 978-80-87990-22-3.
[International Days of Statistics and Economics /14./. Prague (CZ), 10.09.2020-12.09.2020]
R&D Projects: GA ČR(CZ) GA19-05704S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : regression * neural networks * robustness * high-dimensional data * regularization
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://msed.vse.cz/msed_2020/article/252-Kalina-Jan-paper.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0313657