Nonlinear least squares
Nonlinear Least Squares Estimation of the Shifted Gompertz Distribution
... best nonlinear least squares estimate for the shifted Gompertz ...the least squares estimate are obtained, as well as their generalization in the l p norm (1 ≤ p < ...
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Estimation and inference in unstable nonlinear least squares models
... to nonlinear models. To that end, we consider a nonlinear model that can be estimated via nonlinear least squares (NLS) and features a limited number of parameter shifts occur- ring at ...
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A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems
... The nonlinear least squares estimator for the unknown para- meters of nonlinear rational systems has been developed via a standard two-step estimator in the ...
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Regularization methods for the solution of a nonlinear least-squares problem in tomography
... high-dimensional nonlinear least-squares problem, that is much more complex than a linear least-squares one and needs for its resolution a relevant quantity of storage and ...
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Newton Krylov Type Algorithm for Solving Nonlinear Least Squares Problems
... Correspondence should be addressed to Mohammedi R. Abdel-Aziz, mr [email protected] Received 15 December 2008; Accepted 2 February 2009 Recommended by Irena Lasiecka The minimization of a quadratic function within an ...
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Solving Nonlinear Least Squares Problem Using Gauss-Newton Method
... a nonlinear function, an observation model is proposed to approximate the solution of the nonlinear function as closely as ...the nonlinear function and the model proposed is constructed, and the sum ...
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NLINLS: a Differential Evolution based nonlinear least squares Fortran 77 program
... Introduction: Curve fitting or estimation by nonlinear least squares is a difficult task. There are two types of algorithm that are often used for this purpose: those that need evaluation of ...
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NLINLS: a Differential Evolution based nonlinear least squares Fortran 77 program
... Introduction: Curve fitting or estimation by nonlinear least squares is a difficult task. There are two types of algorithm that are often used for this purpose: those that need evaluation of ...
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Numerical Analysis in Nonlinear Least Squares Methods and Applications
... When the numerical methods are applied to solve the two-variable and the multi-variable test problems, the numerical results indicate that both the AGD and the AGDN methods have shown en[r] ...
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Estimation and Inference in Unstable Nonlinear Least Squares Models (Final)
... Chapter 9 Conclusions We considered a univariate NLS models with multiple parameter changes that occur at un- known dates. If the number of breaks is known, we showed that, similar to the linear model of Bai and Perron ...
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Asymptotic normality of nonlinear least squares under singular experimental designs
... However, even in linear models, weak convergence is not enough to ensure regular asymptotic normality of the estimator when the limiting design is singular.. This is illustrated by the e[r] ...
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Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically
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On limited memory SQP methods for large scale constrained nonlinear least squares
... Contents C901 nonlinear programming problem with a simple objective. This is then solved by a limited memory variation of sqp methods. This overcomes one of the major drawbacks of the traditional sqp method, where ...
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The Method of Least Squares
... imation of the function and refine this approximation by succes- sive corrections. The techniques involved are known as gradient descent and Gauss-Newton approximations. They correspond to nonlinear least ...
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Bounded-variable least-squares methods for linear and nonlinear model predictive control
... the nonlinear case in Chapter 3 where we discussed simple box-constrained nonlinear least-squares problem formulations that result in a special sparse structure of the matrices ...eliminate ...
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Nonlinear partial least squares
... These two techniques can handle both underdetermined (fewer observations than variables) data sets and collinearity amongst the variables, by capturing the underlying structure in[r] ...
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THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
... LINEAR REGRESSION LINEAR REGRESSION is a powerfull tool for studying fundamental relationships between two (or more) RVs Y and X. The method is based on the method of least squares. Let’s discuss the ...
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Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
... Regularized robust Nonlinear Least Trimmed Squares es- timator has been proposed in this study by adding an Elastic net penalty to the NLTS loss function. Robust generalized cross-validation was used ...
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Weighted least squares and adaptive least squares: further empirical evidence
... weighted least squares (WLS), also in conjunction with HC standard ...adaptive least squares (ALS), where it is ‘decided’ from the data whether the applied researcher should use either OLS or ...
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