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Smoothing noisy data for irregular regions using penalized bivariate splines on triangulations

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  • Lan Zhou
  • Huijun Pan

Abstract

The penalized spline method has been widely used for estimating univariate smooth functions based on noisy data. This paper studies its extension to the two-dimensional case. To accommodate the need of handling data distributed on irregular regions, we consider bivariate splines defined on triangulations. Penalty functions based on the second-order derivatives are employed to regularize the spline fit and generalized cross-validation is used to select the penalty parameters. A simulation study shows that the penalized bivariate spline method is competitive to some well-established two-dimensional smoothers. The method is also illustrated using a real dataset on Texas temperature. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Lan Zhou & Huijun Pan, 2014. "Smoothing noisy data for irregular regions using penalized bivariate splines on triangulations," Computational Statistics, Springer, vol. 29(1), pages 263-281, February.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:1:p:263-281
    DOI: 10.1007/s00180-013-0448-z
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    References listed on IDEAS

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