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August 14, 2025Journal of Computational and Graphical Statistics

Nonparametric Assessment of Variable Selection and Ranking Algorithms

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Authors

ZTZhou TangTWTed Westling

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Overview

Observational analysis demonstrates improvements in variable selection and ranking algorithms, suggesting better dataset suitability.

Key Points

  • The proposed methods improve finite-sample inference for variable selection and ranking algorithms, enhancing prediction accuracy.
  • Results indicate strong asymptotic properties for the estimators used in variable selection and ranking methods.
  • The study presents a computationally efficient bootstrapping approach for refining statistical inference techniques.
  • Illustrations with wine quality data reveal practical applications for the proposed algorithms across various scientific fields.

Cite This Study

Tang et al. (2025) studied this question.

synapsesocial.com/papers/68af50a7ad7bf08b1ead8e2ehttps://doi.org/10.1080/10618600.2025.2547064
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