Извлечение информации из редких событий в регрессионном анализе

Extracting information from rare events in regression analysis

Oleksiy Dushyn and Borys Dushyn

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigated an important practical problem of extracting information from rare events in sparse and high-dimensional data while building a linear regression model. It analyzes the advantages and the limitations of the different linear regression method used for high-dimensional problems. Main known meth-ods were selected and tested on the real Tripadvisor.com dataset. The results of this research show the impor-tance of the data aggregation based on hierarchical clustering. It allows extracting information from rare fea-tures by aggregating them according the clustering tree. Comparative analyses of main different linear regres-sion methods that use clustering aggregation were done.

Keywords: rare events; regression Analysis; sparse data; high-dimensional data; Lasso; Ridge; ElasticNet; rare methods; text mining; semantic aggregation; hierarchical clustering; vector word representation. (search for similar items in EconPapers)
JEL-codes: C51 C63 C87 (search for similar items in EconPapers)
Date: 2024-02-03
New Economics Papers: this item is included in nep-mac
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