Results 281 to 290 of about 448,585 (316)

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

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
openaire   +3 more sources

Fast and Stable Multiple Smoothing Parameter Selection in Smoothing Spline Analysis of Variance Models With Large Samples

Journal of Computational and Graphical Statistics, 2015
Nathaniel E Helwig, Ping Ma
exaly   +2 more sources

Smoothing Parameter Selection

2009
Paul P. B. Eggermont, Vincent N. LaRiccia   +1 more
openaire   +2 more sources

Smoothing Parameter Selection and Inference

2011
Yuedong Wang
openaire   +2 more sources

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

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

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
openaire   +1 more source

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   +2 more sources