Results 251 to 260 of about 205,425 (288)

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.
Thomas C M Lee
openaire   +3 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, V. N. LaRiccia
exaly   +5 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   +3 more sources

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
openaire   +3 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

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

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.
M. P. Wand, R. G. Gutierrez
openaire   +1 more source