Bernoulli

  • Bernoulli
  • Volume 8, Number 4 (2002), 475-489.

Application of structural risk minimization to multivariate smoothing spline regression estimates

Michael Kohler, Adam Krzyzak, and Dominik Schäfer

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Abstract

Estimation of regression functions from bounded, independent and identically distributed data is considered. Motivated by Vapnik's principle of structural risk minimization, a data-dependent choice of the smoothing parameter of multivariate smoothing spline estimates is proposed. The corresponding smoothing spline estimates automatically adapt to the unknown smoothness of the regression function and their $L^2$ errors achieve the optimal rate of convergence up to a logarithmic factor. The result is valid without any regularity conditions on the distribution of the design.

Article information

Source
Bernoulli Volume 8, Number 4 (2002), 475-489.

Dates
First available in Project Euclid: 7 March 2004

Permanent link to this document
http://projecteuclid.org/euclid.bj/1078681380

Mathematical Reviews number (MathSciNet)
MR2003e:62069

Zentralblatt MATH identifier
1003.62035

Keywords
empirical process theory rate of convergence regression estimate smoothing splines structural risk minimization

Citation

Kohler, Michael; Krzyzak, Adam; Schäfer, Dominik. Application of structural risk minimization to multivariate smoothing spline regression estimates. Bernoulli 8 (2002), no. 4, 475--489. http://projecteuclid.org/euclid.bj/1078681380.


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