Results 231 to 240 of about 34,984 (259)

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

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   +2 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

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
openaire   +2 more sources

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
openaire   +1 more source

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.
openaire   +1 more source

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

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   +2 more sources