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

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>

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

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50204 - Business and management

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)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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