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Gradient Based Smoothing Parameter Selection for Nonparametric Regression Estimation [PDF]

, 2013
Uncovering gradients is of crucial importance across a broad range of economic environments. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. The procedure developed here is automatic and does not require initial estimation of unknown functions with pilot bandwidths.
Daniel J. Henderson, Qi Li, Christopher F. Parmeter   +2 more
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An innovative procedure for smoothing parameter selection

2012
Smoothing with penalized splines calls for an automatic method to select the size of the penalty parameter λ. We propose a not well known smoothing parameter selection procedure: the L-curve method. AIC and (generalized) cross validation represent the most common choices in this kind of problems even if they indicate light smoothing when the data ...
Frasso, Gianluca, Eilers, Paul H.C.
openaire   +1 more source

Smoothing parameter selection using the L-curve

2012
The L-curve method has been used to select the penalty parameter in ridge regression. We show that it is also very attractive for smoothing, because of its low computational load. Surprisingly, it also is almost insensitive to serial correlation.
Frasso, Gianluca, Eilers, Paul H.C.
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The Smoothing Parameter Selection Problem in Smoothing Spline Regression for Different Data Sets

2007
This paper studies smoothing parameter selection problem in nonparametric regression based on smoothing spline method for different data sets. For this aim, a Monte Carlo simulation study was performed. This simulation study provides a comparison of the five popular selection criteria called as cross-validation (CV), generalized cross-validation (GCV),
Aydın, Dursun, Omay, Rabia Ece
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Smoothing Parameters Selection for Samples from Bivariate Circular Distributions

International Journal of Mathematics and Computer Science
Kernel density estimates for bivariate circular data are efficient non-parametric estimation methods incorporating free smoothing parameters that significantly influence the estimation process's results. In this paper, we focus our attention on selecting the optimal bandwidth for bivariate circular data from the von Mises distribution using cross ...
Huda Nasser, Samira Abushilah
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Smoothing Parameter Selection

2009
Paul P. B. Eggermont, Vincent N. LaRiccia   +1 more
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Smoothing Parameter Selection and Inference

2011
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