Results 221 to 230 of about 34,984 (259)

Exact risk approaches to smoothing parameter selection

Journal of Nonparametric Statistics, 1997
The past decade has seen the development of a large number of second-generational smoothing parameter selectors as a response to the high degree of variability of cross-validatory methods. However, most of these rules rely on asymptotic approximations which make them subject to adverse performance when the approximations are poor.
Matt Wand
exaly   +2 more sources

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
exaly   +4 more sources

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, Tutz Gerhard
exaly   +2 more sources

Smoothing parameter selection in hazard estimation

Statistics & Probability Letters, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sarda, P., Vieu, P.
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +2 more sources

Smoothing parameter selection for smooth distribution functions

Journal of Statistical Planning and Inference, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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 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

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.
openaire   +1 more source