High-dimensional data in economics and their (robust) analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00474076" target="_blank" >RIV/67985556:_____/17:00474076 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/17:00473577
Result on the web
<a href="http://dx.doi.org/10.5937/sjm12-10778" target="_blank" >http://dx.doi.org/10.5937/sjm12-10778</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5937/sjm12-10778" target="_blank" >10.5937/sjm12-10778</a>
Alternative languages
Result language
angličtina
Original language name
High-dimensional data in economics and their (robust) analysis
Original language description
This work is devoted to statistical methods for the analysis of economic data with a large number of variables. The authors present a review of references documenting that such data are more and more commonly available in various theoretical and applied economic problems and their analysis can be hardly performed with standard econometric methods. The paper is focused on highdimensional data, which have a small number of observations, and gives an overview of recently proposed methods for their analysis in the context of econometrics, particularly in the areas of dimensionality reduction, linear regression and classification analysis. Further, the performance of various methods is illustrated on a publicly available benchmark data set on credit scoring. In comparison with other authors, robust methods designed to be insensitive to the presence of outlying measurements are also used. Their strength is revealed after adding an artificial contamination by noise to the original data. In addition, the performance of various methods for a prior dimensionality reduction of the data is compared.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
<a href="/en/project/GA17-07384S" target="_blank" >GA17-07384S: Nonparametric (statistical) methods in modern econometrics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Serbian Journal of Management
ISSN
1452-4864
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
1
Country of publishing house
RS - THE REPUBLIC OF SERBIA
Number of pages
13
Pages from-to
171-183
UT code for WoS article
000443474000012
EID of the result in the Scopus database
2-s2.0-85018191894