Results 281 to 290 of about 116,475 (314)
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Semiparametric Generalized Least Squares in the Multivariate Nonlinear Regression Model
Econometric Theory, 1992Asymptotically 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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Nonlinear Regression, Quasi Likelihood, and Overdispersion in Generalized Linear Models
The American Statistician, 1998Abstract 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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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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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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Journal of the American Statistical Association, 2001
In this article, we consider a general multivariate nonlinear regression setting in which the marginal mean and varianceācovariance 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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In this article, we consider a general multivariate nonlinear regression setting in which the marginal mean and varianceācovariance 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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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 +3 more
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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 +3 more
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Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
Technometrics, 1970A 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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IEEE Transactions on Evolutionary Computation, 2009
This paper presents a novel approach to generate data-driven regression models that not only give reliable prediction of the observed data but also have smoother response surfaces and extra generalization capabilities with respect to extrapolation. These models are obtained as solutions of a genetic programming (GP) process, where selection is guided ...
Vladislavleva, E.J. +2 more
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This paper presents a novel approach to generate data-driven regression models that not only give reliable prediction of the observed data but also have smoother response surfaces and extra generalization capabilities with respect to extrapolation. These models are obtained as solutions of a genetic programming (GP) process, where selection is guided ...
Vladislavleva, E.J. +2 more
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Consistency of M-estimates in general nonlinear regression models
2009Nonlinear regression model with continuous time and weak dependent or long-range dependent stationary noise is considered. Strong consistency suffient conditions of M-estimates of regression parameters are obtained.
Ivanov, A.V., Orlovsky, I.V.
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Nonlinear Regression of Ink Spreading Using a General Ellipse Model
2022 22nd International Conference on Control, Automation and Systems (ICCAS), 2022H. Kim, J. Kim, M. Kwon
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Biometrika, 1983
SUMMARY The class of generalized linear models is extended to allow for correlated observations, nonlinear models and error distributions not of the exponential family form. The extended class of models include a number of important examples, particularly of the composite transformational type.
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SUMMARY The class of generalized linear models is extended to allow for correlated observations, nonlinear models and error distributions not of the exponential family form. The extended class of models include a number of important examples, particularly of the composite transformational type.
openaire +3 more sources