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Halton and Hammersley sequences in multivariate nonparametric regression

Author

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  • Rafajlowicz, Ewaryst
  • Schwabe, Rainer

Abstract

The present paper generalizes results by Rafajlowicz and Schwabe [2003. Equidistributed designs in nonparametric regression. Statist. Sinica 13, 129-142] for quasi least squares estimators in additive regression to a general multivariate regression setup. Equidistributed sequences of Halton or Hammersley type provide consistent regression estimators with a satisfactory rate of convergence. As those sequences are easy to construct they can serve as suitable experimental designs. Optimal generators for the Halton and Hammersley sequences are found by exhaustive search.

Suggested Citation

  • Rafajlowicz, Ewaryst & Schwabe, Rainer, 2006. "Halton and Hammersley sequences in multivariate nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 76(8), pages 803-812, April.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:8:p:803-812
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    References listed on IDEAS

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    1. Bates, R. A. & Riccomagno, E. & Schwabe, R. & Wynn, H. P., 1998. "Lattices and dual lattices in optimal experimental design for Fourier models," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 283-296, September.
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