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

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. 2013 Springer-Verlag Berlin Heidelberg.

authors

• Zhou, Lan

published proceedings

• COMPUTATIONAL STATISTICS

author list (cited authors)

• Zhou, L., & Pan, H.

citation count

• 7

complete list of authors

• Zhou, Lan||Pan, Huijun

publication date

• February 2014

publisher

• Springer Nature  Publisher

published in

• Computational Statistics  Journal

keywords

• Bivariate Smoothing
• Generalized Cross-validation
• Nonparametric Function Estimation
• P-splines
• Roughness Penalty

Digital Object Identifier (DOI)

• 10.1007/s00180-013-0448-z

start page

• 263

end page

• 281

volume

• 29

issue

• 1-2

URL

• http://dx.doi.org/10.1007/s00180-013-0448-z