Results 271 to 280 of about 3,817,143 (297)

Smoothing Parameter Selection in Image Restoration

1991
We consider the problem of the automatic selection of the smoothing parameter in image restoration using the method of regularisation. We consider two new smoothing parameter selectors based on the estimation cross-validation function and compare their performance with some others proposed in the literature and also with some optimal methods.
K. P.-S. Chan, J. W. Kay
openaire   +1 more source

Some characteristics on the selection of spline smoothing parameter

Communications in Statistics - Theory and Methods, 2017
ABSTRACTThe smoothing spline method is used to fit a curve to a noisy data set, where selection of the smoothing parameter is essential. An adaptive Cp criterion (Chen and Huang 2011) based on the Stein’s unbiased risk estimate has been proposed to select the smoothing parameter, which not only considers the usual effective degrees of freedom but also ...
Chun-Shu Chen, Yi-Tsz Huang
openaire   +1 more source

Smoothing parameter selection in quasi-likelihood models

Journal of Nonparametric Statistics, 2006
We derive an improved version of the Akaike information criterion (AICC) for quasi-likelihood models with nonparametric functions.
Jeng-Min Chiou, Chih-Ling Tsai
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Nonparametric regression for functional data: Automatic smoothing parameter selection

Journal of Statistical Planning and Inference, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rachdi, Mustapha, Vieu, Philippe
openaire   +4 more sources

New approaches to nonparametric density estimation and selection of smoothing parameters

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nina Golyandina, Andrey Pepelyshev, Ansgar Steland   +2 more
openaire   +3 more sources

Automatic smoothing parameter selection in GAMLSS with an application to centile estimation

Statistical Methods in Medical Research, 2013
A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each ...
Robert A, Rigby, Dimitrios M, Stasinopoulos   +1 more
openaire   +2 more sources

ASYMPTOTIC STABILITY OF THE OSCV SMOOTHING PARAMETER SELECTION

Communications in Statistics - Theory and Methods, 2001
The smoothing parameter selection by the one-sided cross-validation (OSCV) method is completely automatic in that it does not require extra parameters estimation. Also it reduces the variability comparable to that of plug-in rules. In this paper we derive analytically the asymptotic variance of the smoothing parameter selected by OSCV.
openaire   +1 more source

A proposal method for selecting smoothing parameter with missing values

2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE), 2012
In this paper we proposed a new method for selecting a smoothing parameter in kernel estimator to estimate a nonparametric regression function in the presence of missing values. The proposed method is based on work on the golden ratio and Surah AL-E-Imran in the Qur'an. Simulation experiments were conducted to study a small sample behavior. The results
Qutaiba N. Nayef Al-Kazaz, Munaf Yousif Aldahham   +1 more
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Simultaneous selection of variables and smoothing parameters by genetic algorithms

2004
In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use separate toolsto solve the different tasks of variable selection and smoothing parameter choice.
Krause, Rüdiger, Tutz, Gerhard
openaire   +2 more sources

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.
openaire   +1 more source