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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/67985807:_____/17:00473577

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

<a href="/cs/project/GA17-07384S" target="_blank" >GA17-07384S: Neparametrické (statistické) metody v moderní ekonometrii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Serbian Journal of Management

ISSN

1452-4864

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

13

Strana od-do

171-183

Kód UT WoS článku

000443474000012

EID výsledku v databázi Scopus

2-s2.0-85018191894