Počet záznamů: 1  

High-dimensional data in economics and their (robust) analysis

  1. 1.
    SYSNO ASEP0474076
    Druh ASEPJ - Článek v odborném periodiku
    Zařazení RIVJ - Článek v odborném periodiku
    Poddruh JČlánek ve WOS
    NázevHigh-dimensional data in economics and their (robust) analysis
    Tvůrce(i) Kalina, Jan (UTIA-B)
    Zdroj.dok.Serbian Journal of Management. - : Univerzitet u Beogradu - ISSN 1452-4864
    Roč. 12, č. 1 (2017), s. 171-183
    Poč.str.13 s.
    Forma vydáníTištěná - P
    Jazyk dok.eng - angličtina
    Země vyd.RS - Srbsko
    Klíč. slovaeconometrics ; high-dimensional data ; dimensionality reduction ; linear regression ; classification analysis ; robustness
    Vědní obor RIVBA - Obecná matematika
    Obor OECDBusiness and management
    CEPGA17-07384S GA ČR - Grantová agentura ČR
    Institucionální podporaUTIA-B - RVO:67985556
    UT WOS000443474000012
    EID SCOPUS85018191894
    DOI10.5937/sjm12-10778
    AnotaceThis 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.
    PracovištěÚstav teorie informace a automatizace
    KontaktMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Rok sběru2018
Počet záznamů: 1  

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