guest ::
| Home > Conference materials > Papers > Some Robust Approaches to Reducing the Complexity of Economic Data |
Original title:
Some Robust Approaches to Reducing the Complexity of Economic Data
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /17./, Praha (CZ), 20230907
Year: 2023
Language: eng
Abstract: 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.
Keywords: Big Data; dimensionality reduction; robustness; sparsity; variable selection
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: The 17th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-31-5
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2023/SI/kalina-0583575.pdf
Original record: https://hdl.handle.net/11104/0351582
Permalink: http://www.nusl.cz/ntk/nusl-541553
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /17./, Praha (CZ), 20230907
Year: 2023
Language: eng
Abstract: 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.
Keywords: Big Data; dimensionality reduction; robustness; sparsity; variable selection
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: The 17th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-31-5
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2023/SI/kalina-0583575.pdf
Original record: https://hdl.handle.net/11104/0351582
Permalink: http://www.nusl.cz/ntk/nusl-541553
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
Record created 2024-03-10, last modified 2024-04-15