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Извлечение Информации Из Редких Событий В Регрессионном Анализе
[Extracting information from rare events in regression analysis]

Author

Listed:
  • Dushyn, Oleksiy
  • Dushyn, Borys

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.

Suggested Citation

  • Dushyn, Oleksiy & Dushyn, Borys, 2024. "Извлечение Информации Из Редких Событий В Регрессионном Анализе [Extracting information from rare events in regression analysis]," MPRA Paper 120235, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:120235
    as

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    File URL: https://mpra.ub.uni-muenchen.de/120235/9/MPRA_paper_120235.pdf
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    References listed on IDEAS

    as
    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    rare events; regression Analysis; sparse data; high-dimensional data; Lasso; Ridge; ElasticNet; rare methods; text mining; semantic aggregation; hierarchical clustering; vector word representation.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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