ridge.diacomp: Calculation of Generalized Cross-validation

In SpherWave: Spherical Wavelets and SW-based Spatially Adaptive Methods

Description

This function calculates generalized cross-validation for ridge regression.

Usage

ridge.diacomp(out.ls, obs, lam) 

Arguments

out.ls	an object of class ‘lsfit’
obs	observations
lam	smoothing parameter for penalized least squares method

Details

This function calculates generalized cross-validation for ridge regression.

Value

rsq	R-squared
gcv	generalized cross-validation for ridge regression.
df	degree of freedom

References

Oh, H-S. (1999) Spherical wavelets and their statistical analysis with applications to meteorological data. Ph.D. Thesis, Department of Statistics, Texas A\&M University, College Station.

Li, T-H. (1999) Multiscale representation and analysis of spherical data by spherical wavelets. SIAM Journal on Scientific Computing, 21, 924–953.

Oh, H-S. and Li, T-H. (2004) Estimation of global temperature fields from scattered observations by a spherical-wavelet-based spatially adaptive method. Journal of the Royal Statistical Society Ser. B, 66, 221–238.

See Also

gcv.lambda, ridge.comp.

SpherWave documentation built on April 14, 2017, 1:28 p.m.