Results 261 to 270 of about 204,377 (277)

Smoothing parameter selection for smoothing splines: a simulation study

Computational Statistics & Data Analysis, 2003
Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods.
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A note on smoothing parameter selection for penalized spline smoothing

Journal of Statistical Planning and Inference, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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REGRESSION SMOOTHING PARAMETER SELECTION USING CROSS RESIDUALS SUM

Communications in Statistics - Theory and Methods, 2002
ABSTRACT In this paper the well-known regression smoothing parameter selection problem is revisited. Rice (Rice, J. Bandwidth Choice for Nonparametric Regression. The Annals of Statistics 1984, 12, 1215–1230.) and Hardle et al. (Hardle, W.; Hall, P.; Marron, J.S. How Far Are Automatically Chosen Regression Parameters from Their Optimum?
Tae Yoon Kim, Cheolyong Park
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Adaptive testing using data-driven method selecting smoothing parameters

Economics Letters, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Nonlinearly Smoothed EM Density Estimation with Automated Smoothing Parameter Selection for Nonparametric Deconvolution Problems

Journal of the American Statistical Association, 1997
Abstract We study a nonparametric deconvolution density estimation problem. The estimator is obtained by an EM algorithm for a smoothed maximum likelihood estimation problem, which has a unique continuous solution. We present an implementation of the procedure incorporating a data-driven discrepancy principle for selecting the smoothing parameter ...
P. P. B. Eggermont, V. N. LaRiccia
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Gradient-based smoothing parameter selection for nonparametric regression estimation

Journal of Econometrics, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Henderson, Daniel J., Li, Qi, Parmeter, Christopher F., Yao, Shuang   +3 more
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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
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Automatic smoothing parameter selection in non‐parametric models for longitudinal data

Applied Stochastic Models and Data Analysis, 1997
An automatic smoothing parameter selection procedure, known as BRUTO, that uses a modified version of the generalized cross-validation (GCV) criterion by exploiting the advantages of the backfitting algorithm is not directly applicable to longitudinal data since the use of GCV leads to undersmoothing or oversmoothing depending on the nature of the ...
Berhane, Kiros, Rao, J. Sunil
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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.
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Generalized Nonparametric Mixed-Effect Models: Computation and Smoothing Parameter Selection

Journal of Computational and Graphical Statistics, 2005
Generalized linear mixed-effect models are widely used for the analysis of correlated non-Gaussian data such as those found in longitudinal studies. In this article, we consider extensions with nonparametric fixed effects and parametric random effects.
Chong Gu, Ping Ma
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