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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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