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May 8, 2026SymmetryOpen Access

The k-Nearest-Neighbor Smoothing Estimator for Functional Least Absolute Relative Error Regression

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Authors

ZKZoulikha KaidKing Khalid UniversityFAFatimah A. AlmulhimPrincess Nourah bint Abdulrahman UniversityMAMohammed B. AlamariKing Khalid University

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Implication

A new nonparametric regression method shows improved accuracy in functional data using kNN and LARE.

Key Points

  • This study aims to develop a robust nonparametric estimator for regression using functional covariates with a focus on minimizing prediction error.
  • Proposed a k-nearest neighbor (kNN) smoothing estimator based on the least absolute relative error (LARE) criterion.
  • Established the uniform convergence of the estimator under mild conditions.
  • Illustrated the estimator's effectiveness using simulation experiments and application to NIR spectrometry data.
  • Achieved symmetric penalties for overestimation and underestimation, enhancing robustness.
  • Demonstrated optimal selection of the number of neighbors significantly impacts estimation performance.
  • Showed improved prediction accuracy compared to classical regression methods.

Cite This Study

Kaid et al. (2026) studied this question.

synapsesocial.com/papers/69fd7f86bfa21ec5bbf07fc7https://doi.org/10.3390/sym18050790
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Also Consider

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