Results 261 to 270 of about 428,031 (297)

A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity

Journal of Regional Science, 2001
There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques.
Thomas De Graaff, Raymond J.C.M. Florax, Peter Nijkamp, Aura Reggiani   +3 more
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Fitting De Wit competition models with general nonlinear regression programs

Ecological Modelling, 1988
Abstract Replacement series competition studies are frequently analyzed with specially designed computer programs. Commonly available nonlinear regression programs can be used to analyze such experiments with little loss of efficiency, if two indicator variables are added to the data.
Lawrence C. Larsen, William A. Williams
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High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm

Proceedings of the AAAI Conference on Artificial Intelligence
Overparameterization often leads to benign overfitting, where deep neural networks can be trained to overfit the training data but still generalize well on unseen data. However, it lacks a generalized asymptotic framework for nonlinear regressions and connections to conventional complexity notions.
Jian Li 0040, Yong Liu 0018, Weiping Wang 0005   +2 more
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Semiparametric Generalized Least Squares in the Multivariate Nonlinear Regression Model

Econometric Theory, 1992
Asymptotically efficient estimates for the multiple equations nonlinear regression model are obtained in the presence of heteroskedasticity of unknown form. The proposed estimator is a generalized least squares based on nonparametric nearest neighbor estimates of the conditional variance matrices. Some Monte Carlo experiments are reported.
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A Predictive Model of Nonlinear System Based on Generalized Regression Neural Network

2005 International Conference on Neural Networks and Brain, 2006
Generalized regression neural network (GRNN) is usually applied to the function approximation. Based on the principle of GRNN, this paper presents a method for the predictive model of nonlinear complex system. The presented algorithm is applied to the training and predicting process of the nonlinear model.
null Yibin Song, null Ying Ren
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Nonlinear Regression, Quasi Likelihood, and Overdispersion in Generalized Linear Models

The American Statistician, 1998
Abstract The aim of this article is to reconsider the methods for handling of overdispersion in generalized linear models proposed by McCullagh and Nelder. Our starting point will be a nonlinear regression model with normal errors, specified by a mean function, a variance function and a matrix of covariates.
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Generalized multiple-regression techniques with interaction and nonlinearity for system identification in biological treatment processes

ISA Transactions, 1992
A class of multiple regression models, called "generalized multiple-regression" (GMR) is proposed. GMR has the advantages of being easy and rapid to fit, and uses standard multilinear regression software. It has an advantage over ARIMA models in modeling nonlinearity and linear and nonlinear interactions among variables.
D A, Vaccari, C, Christodoulatos
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Generalized Least Squares, Taylor Series Linearization and Fisher's Scoring in Multivariate Nonlinear Regression

Journal of the American Statistical Association, 2001
In this article, we consider a general multivariate nonlinear regression setting in which the marginal mean and variance–covariance structure share a common set of regression parameters. Estimation is carried out via iteratively reweighted generalized least squares (IRGLS) that entails repeated application of Taylor series linearization and estimated ...
Vonesh E. F, Wang H., Majumdar D.
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A generalized-constraint neural network model: Associating partially known relationships for nonlinear regressions☆

Information Sciences, 2009
In an attempt to enhance the neural network technique so that it can evolve from a ''black box'' tool into a semi-analytical one, we propose a novel modeling approach of imposing ''generalized constraints'' on a standard neural network. We redefine approximation problems by use of a new formalization with the aim of embedding prior knowledge explicitly
Bao-Gang Hu, Han-Bing Qu, Yong Wang, Shuang-Hong Yang   +3 more
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Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation

Technometrics, 1970
A principal objective of this paper is to discuss a class of biased linear estimators employing generalized inverses. A second objective is to establish a unifying perspective. The paper exhibits theoretical properties shared by generalized inverse estimators, ridge estimators, and corresponding nonlinear estimation procedures. From this perspective it
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