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

  • Authors:

  • Lan Zhou;Huijun Pan

  • Affiliations:

  • Department of Statistics, 3143 TAMU, Texas A&M University, College Station, USA 77843;Travelers, Hartford, USA 06183

  • Venue:
  • Computational Statistics
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

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.