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A note on smoothing parameter selection for penalized spline smoothing

Journal of Statistical Planning and Inference, 2005
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Kauermann, Göran
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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
exaly   +4 more sources

Smoothing parameter selection for nonparametric regression using smoothing spline

, 2013
In this paper, the smoothing parameter selection problem has been examined in respect to a smoothing spline implementation in predicting nonparametric regression models. For this purpose, a simulation study has been performed by using a program written in MATLAB.
Aydin, Dursun, Memmedli, Memmedaga, Omay, Rabia Ece   +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.
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Simultaneous Selection of Variables and Smoothing Parameters in Additive Models

2005
For additive models of the type y = f1(x1) + … + fP(xp) + e where fj,j = 1, …, p, have unspecified functional form the problem of variable selection is strongly connected to the choice of the amount of smoothing used for components fj. In this paper we propose the simultaneous choice of variables and smoothing parameters based on genetic algorithms ...
Gerhard Tutz
exaly   +2 more sources

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 parameter selection in hazard estimation

Statistics & Probability Letters, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sarda, P., Vieu, P.
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How to Select the Smoothing Parameter?

1989
From the results given in the previous sections it appeared that the bandwidth h played a dominant role in the behaviour of kernel estimates for regression, density or hazard function estimation.
Lázió Györfi, Wolfgang Härdle, Pascal Sarda, Philippe Vieu   +3 more
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Smoothing parameter selection for smooth distribution functions

Journal of Statistical Planning and Inference, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Genetic algorithms for the selection of smoothing parameters in additive models

Computational Statistics, 2006
A nonparametric additive regression model is considered of the form \[ y_t=\beta_0+\sum_{j=1}^p f_j(x_{ij})+\varepsilon_i, \] where \(y_i\) is the response, \(x_{ij}\) are regressors, and \(f_j\) are unknown regression functions to be estimated via local spline smoothing.
Rüdiger Krause, Gerhard Tutz 0001
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