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Near G-optimal Tchakaloff designs

Author

Listed:
  • Len Bos

    (University of Verona)

  • Federico Piazzon

    (University of Padova)

  • Marco Vianello

    (University of Padova)

Abstract

We show that the notion of polynomial mesh (norming set), used to provide discretizations of a compact set nearly optimal for certain approximation theoretic purposes, can also be used to obtain finitely supported near G-optimal designs for polynomial regression. We approximate such designs by a standard multiplicative algorithm, followed by measure concentration via Caratheodory-Tchakaloff compression.

Suggested Citation

  • Len Bos & Federico Piazzon & Marco Vianello, 2020. "Near G-optimal Tchakaloff designs," Computational Statistics, Springer, vol. 35(2), pages 803-819, June.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:2:d:10.1007_s00180-019-00933-8
    DOI: 10.1007/s00180-019-00933-8
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    References listed on IDEAS

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    1. Sommariva, Alvise & Vianello, Marco, 2015. "Polynomial fitting and interpolation on circular sections," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 410-424.
    2. Dette, Holger & Pepelyshev, Andrey & Zhigljavsky, Anatoly, 2008. "Improving updating rules in multiplicative algorithms for computing D-optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 312-320, December.
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    Cited by:

    1. Monica Dessole & Fabio Marcuzzi & Marco Vianello, 2020. "dCATCH—A Numerical Package for d-Variate near G-Optimal Tchakaloff Regression via Fast NNLS," Mathematics, MDPI, vol. 8(7), pages 1-15, July.

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