Speakers
Description
The subject of this presentation concerns the implementation of regularized regression and advanced parameter estimation procedures. The study demonstrates the practical application of these methods using three R packages developed by the authors: S-type.est, Styperidge.reg, and ridgregextra.
Within this ecosystem, the S-type.est package provides the core estimation procedures for S-type estimators, serving as the methodological foundation. The Styperidge.reg package implements S-type regularized models for classical linear regression settings, while ridgregextra focuses on ridge regression as a special case, offering extended tools for estimation and diagnostic analysis.
To showcase the utility of these packages in complex data environments, we present a case study on energy demand prediction. Energy forecasting often involves high-dimensional, highly correlated predictors—such as meteorological variables and historical consumption patterns—where standard OLS estimators frequently fail. The combined use of these packages enables a comprehensive examination of ridge and S-type regularization, emphasizing usability, reproducibility, and methodological clarity in addressing the challenges of energy-related statistical research.
Additional Material or Paper
CRAN Links of the packages developed by us and are the main context of the study.
https://cran.r-project.org/web/packages/Stype.est/index.html
https://cran.r-project.org/web/packages/Styperidge.reg/index.html
https://cran.r-project.org/web/packages/ridgregextra/index.html
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | ridge regression, robust regression, S-Type Estimators, S-Type regression |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |