Regression for High-Dimensional Data: From Regularization to Deep Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00535704" target="_blank" >RIV/67985807:_____/20:00535704 - isvavai.cz</a>
Result on the web
<a href="https://msed.vse.cz/msed_2020/article/252-Kalina-Jan-paper.pdf" target="_blank" >https://msed.vse.cz/msed_2020/article/252-Kalina-Jan-paper.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Regression for High-Dimensional Data: From Regularization to Deep Learning
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 14th International Days of Statistics and Economics Conference Proceedings
ISBN
978-80-87990-22-3
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
418-427
Publisher name
Melandrium
Place of publication
Slaný
Event location
Prague
Event date
Sep 10, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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