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Název:
Some Robust Approaches to Reducing the Complexity of Economic Data
Autoři: Kalina, Jan
Typ dokumentu: Příspěvky z konference
Konference/Akce: International Days of Statistics and Economics /17./, Praha (CZ), 20230907
Rok: 2023
Jazyk: eng
Abstrakt: 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.
Klíčová slova: Big Data; dimensionality reduction; robustness; sparsity; variable selection
Číslo projektu: GA21-05325S (CEP)
Poskytovatel projektu: GA ČR
Zdrojový dokument: The 17th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-31-5
Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: https://msed.vse.cz/msed_2023/article/687-Kalina-Jan-paper.pdf
Původní záznam: https://hdl.handle.net/11104/0349888
Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-538554
Záznam je zařazen do těchto sbírek:
Věda a výzkum > AV ČR > Ústav informatiky
Konferenční materiály > Příspěvky z konference
Autoři: Kalina, Jan
Typ dokumentu: Příspěvky z konference
Konference/Akce: International Days of Statistics and Economics /17./, Praha (CZ), 20230907
Rok: 2023
Jazyk: eng
Abstrakt: 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.
Klíčová slova: Big Data; dimensionality reduction; robustness; sparsity; variable selection
Číslo projektu: GA21-05325S (CEP)
Poskytovatel projektu: GA ČR
Zdrojový dokument: The 17th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-31-5
Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: https://msed.vse.cz/msed_2023/article/687-Kalina-Jan-paper.pdf
Původní záznam: https://hdl.handle.net/11104/0349888
Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-538554
Záznam je zařazen do těchto sbírek:
Věda a výzkum > AV ČR > Ústav informatiky
Konferenční materiály > Příspěvky z konference
Záznam vytvořen dne 2024-01-25, naposledy upraven 2024-11-27.