Results 261 to 270 of about 116,475 (314)

Generalized Nonlinear Modeling With Multivariate Free-Knot Regression Splines

open access: yesJournal of the American Statistical Association, 2003
A Bayesian method is presented for the nonparametric modeling of univariate and multivariate non-Gaussian response data. Data-adaptive multivariate regression splines are used where the number and location of the knot points are treated as random.
B K Mallick
exaly   +3 more sources
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Asymptotic Confidence Bands for Generalized Nonlinear Regression Models

Biometrics, 1995
Asymptotic confidence bands for generalized nonlinear regression models are developed. These are based on a combination of the S method of Scheffe, together with the delta method which is used to approximate the mean function by a linear combination of the parameters. The approach can be used in any situation where large sample theory can be applied to
Cox, Christopher, Ma, Guangqin
openaire   +3 more sources

A general framework for robust compressive sensing based nonlinear regression

2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012
In this paper, we present a general framework for robust nonlinear regression that leverages concepts from the field of compressive sensing to simultaneously detect outliers and determine optimally sparse representations of noisy data from arbitrary sets of basis functions.
Brian Moore, Balasubramaniam Natarajan
openaire   +1 more source

Structural identifiability of generalized constraint neural network models for nonlinear regression

Neurocomputing, 2008
Identifiability becomes an essential requirement for learning machines when the models contain physically interpretable parameters. This paper presents two approaches to examining structural identifiability of the generalized constraint neural network (GCNN) models by viewing the model from two different perspectives.
Shuang-Hong Yang   +2 more
openaire   +2 more sources

Nonlinear process monitoring based on decentralized generalized regression neural networks

Expert Systems with Applications, 2020
Abstract Given that the main task of process monitoring (i.e., fault detection) is actually a classical one-class classification problem, the generalized regression neural network (GRNN) is directly inapplicable for handling process modeling and monitoring issues.
Ting Lan 0001   +4 more
openaire   +1 more source

On generalized elliptical quantiles in the nonlinear quantile regression setup

TEST, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hlubinka, Daniel, Šiman, Miroslav
openaire   +1 more source

Confirmation of multiple outliers in generalized linear and nonlinear regressions

Computational Statistics & Data Analysis, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fung, WK, Lee, AH
openaire   +2 more sources

Generalized Linear and Nonlinear Regression

2014
Multiple linear regression is a powerful method of exploring relationships between a response Y and a set of potential explanatory variables \(x_1,\ldots , x_p\), but it has an obvious limitation: it assumes the predictive relationship is, on average, linear.
Robert E. Kass   +2 more
openaire   +1 more source

Robust estimation and testing for general nonlinear regression models

Applied Mathematics and Computation, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tabatabai, M. A., Argyros, I. K.
openaire   +2 more sources

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