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Variable Selection

In: Applied Multivariate Statistical Analysis

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics)

  • Léopold Simar

    (Université Catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences)

  • Matthias R. Fengler

    (University of St. Gallen, School of Economics and Political Science)

Abstract

Effective variable selection plays a pivotal role in statistical modeling. We are frequently not only interested in using a model for prediction, but also need to correctly identify the relevant variables, i.e., to recover the correct model under given assumptions. It is known that under certain conditions, the ordinary least squares (OLS) method produces poor prediction results and does not yield a parsimonious model, resulting in overfitting. The objective of variable selection methods is to find the variables which are the most relevant ones for prediction. Such methods are particularly valuable, when the true underlying model has a sparse representation where many parameters are close to zero. The identification of relevant variables reduces noise and therefore improves the predictive performance of the model.

Suggested Citation

  • Wolfgang Karl Härdle & Léopold Simar & Matthias R. Fengler, 2024. "Variable Selection," Springer Books, in: Applied Multivariate Statistical Analysis, edition 0, chapter 0, pages 269-293, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-63833-6_9
    DOI: 10.1007/978-3-031-63833-6_9
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