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THE GENERALIZED INVERSE, WITH NONLINEAR REGRESSION AND MATHEMATICAL PROGRAMMING APPLICATIONS
Decision Sciences, 1975This paper is a tutorial exposition on the generalized inverse of a matrix with typical applications to regression analysis and mathematical programming. The exposition contains examples exhibiting geometrical motivation and related facts useful in application of the generalized inverse.
Henry P. Decell, Elric N. McHenry
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General Estimates of the Intrinsic Variability of Data in Nonlinear Regression Models
Journal of the American Statistical Association, 1976Abstract A dependent variable is some unknown function of independent variables plus an error component. If the magnitude of the error could be estimated with minimal assumptions about the underlying functional dependence, then this could be used to judge goodness-of-fit and as a means of selecting a subset of the independent variables which best ...
L. Breiman, W. S. Meisel
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NL: A Statistical Package for General Nonlinear Regression Problems
1986NL is a statistical package designed for nonlinear regression problems, taking into account the heteroscedasticity of variance, if any. The algorithm for the estimation of the regression parameters is adapted to the topic but it also presents possibilities for future extensions.
S. Huet, A. Messéan
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Nonlinear regression technique applied to generalized phase-shifting interferometry
Journal of Modern Optics, 2005We develop a new approach involving nonlinear regression in phase-shifting interferometry with the hope of improving the accuracy of phase measurement in the presence of first-order piezoelectric transducer (PZT) calibration errors. The approach that uses the Levenberg–Marquardt method is shown to detect with high precision the value of the true phase ...
Abhijit Patil, Pramod Rastogi
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Communications in Statistics - Simulation and Computation, 2019
In this paper, by considering an (n+1)-dimensional random vector (X1,X2,….,Xn,Y)T from the multivariate generalized hyperbolic (GH) distribution, we derive the joint distribution of Y and the order...
Mehdi Amiri +2 more
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In this paper, by considering an (n+1)-dimensional random vector (X1,X2,….,Xn,Y)T from the multivariate generalized hyperbolic (GH) distribution, we derive the joint distribution of Y and the order...
Mehdi Amiri +2 more
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Eye Gaze Calculation Based on Nonlinear Polynomial and Generalized Regression Neural Network
2009 Fifth International Conference on Natural Computation, 2009In this paper, we present a method of calculating the direction of the line of sight, which is based on nonlinear polynomial and generalized regression neural network, using a active infrared light source system. First of all, we get a model to map the gaze parameter to the gaze point under the circumstances of a static head with nonlinear polynomial ...
Jian-Nan Chi +4 more
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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 +3 more
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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 +3 more
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Fitting De Wit competition models with general nonlinear regression programs
Ecological Modelling, 1988Abstract 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 IntelligenceOverparameterization 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 +2 more
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A Predictive Model of Nonlinear System Based on Generalized Regression Neural Network
2005 International Conference on Neural Networks and Brain, 2006Generalized 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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