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A Hermite spline model for data regression

Author

Listed:
  • Campagna, Rosanna
  • Cotronei, Mariantonia
  • Fazzino, Domenico

Abstract

This paper introduces a novel Hermite spline model for data regression, integrating both function values and derivatives along with a penalty term to control smoothness. A comparative analysis is conducted with conventional penalized models, specifically with P-spline models. The primary objective of this study is to empirically demonstrate the superior performance of the proposed model in reconstructing data, even in the absence of a penalty term (pure regression).

Suggested Citation

  • Campagna, Rosanna & Cotronei, Mariantonia & Fazzino, Domenico, 2025. "A Hermite spline model for data regression," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 229(C), pages 222-234.
    • Handle: RePEc:eee:matcom:v:229:y:2025:i:c:p:222-234
    DOI: 10.1016/j.matcom.2024.09.011
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    References listed on IDEAS

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    1. Campagna, Rosanna & Conti, Costanza & Cuomo, Salvatore, 2023. "A linear algebra approach to HP-splines frequency parameter selection," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    2. Mariantonia Cotronei & Caroline Moosmüller, 2021. "Hermite B-Splines: n -Refinability and Mask Factorization," Mathematics, MDPI, vol. 9(19), pages 1-11, October.
    3. Bertolazzi, Enrico & Frego, Marco & Biral, Francesco, 2020. "Point data reconstruction and smoothing using cubic splines and clusterization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 176(C), pages 36-56.
    4. Daniel Gervini, 2006. "Free‐knot spline smoothing for functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(4), pages 671-687, September.
    Full references (including those not matched with items on IDEAS)

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