Some Robust Approaches to Reducing the Complexity of Economic Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00583575" target="_blank" >RIV/67985556:_____/23:00583575 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/23:00581699
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Some Robust Approaches to Reducing the Complexity of Economic Data
Original language description
The recent advent of complex (and potentially big) data in economics requires modern and effective tools for their analysis including tools for reducing the dimensionality (complexity) of the given data. This paper starts with recalling the importance of Big Data in economics and with characterizing the main categories of dimension reduction techniques. While there have already been numerous techniques for dimensionality reduction available, this work is interested in methods that are robust to the presence of outlying measurements (outliers) in the economic data. Particularly, methods based on implicit weighting assigned to individual observations are developed in this paper. As the main contribution, this paper proposes three novel robust methods of dimension reduction. One method is a dimension reduction within a robust regularized linear regression, namely a sparse version of the least weighted squares estimator. The other two methods are robust versions of feature extraction methods popular in econometrics: robust principal component analysis and robust factor analysis.
Czech name
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Czech description
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Project
<a href="/en/project/GA21-05325S" target="_blank" >GA21-05325S: Modern nonparametric methods in econometrics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
The 17th International Days of Statistics and Economics Conference Proceedings
ISBN
978-80-87990-31-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
246-255
Publisher name
Melandrium
Place of publication
Praha
Event location
Praha
Event date
Sep 7, 2023
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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