0583575 20240402215301.720240305235959.9 10 s. P cav_un_epca*0581698978-80-87990-31-5246-255PrahaMelandrium2023LösterT.PavelkaT. dimensionality reduction Big Data variable selection robustness sparsity cav_un_auth*0345793 Kalina Jan UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/SI/kalina-0583575.pdf GA21-05325S GA ČR cav_un_auth*0409039 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. cav_un_auth*0462041 International Days of Statistics and Economics /17./ 20230907 20230909 Praha CZ BB 10000 10100 10103 2024 PR RVO:67985556 https://hdl.handle.net/11104/0351582 S 2023 SCOPUS PUBMED WOS cav_un_epca*0581698 The 17th International Days of Statistics and Economics Conference Proceedings Melandrium 2023 Praha 246 255 978-80-87990-31-5 Löster T. 340 Pavelka T. 340