Nonlinear least squares and maximum likelihood models
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- ...
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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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A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems
... For nonlinear rational systems, how- ever, the resulting noise is a function of the ...linear least squares compen- sation (Stoica & S¨oderstr¨om, ...are nonlinear systems, both noise variance ...
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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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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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The Maximum Likelihood Stage Least Squares Estimator in the Nonlinear Simultaneous Equations Model
... Research supported by NSF Grant DCR 70_03L56 A04 to the National Bureau of Economic Research, Inc... The consistency and the asymptotic normality of the maximum likelihood estintor in th[r] ...
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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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Estimation and Inference in Unstable Nonlinear Least Squares Models (Final)
... Chapter 1 Introduction The issue of structural instability has received much attention from economists in the last few decades. Because of events such as the Great Depression, oil price shocks, the Great Moderation, ...
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Numerical Analysis in Nonlinear Least Squares Methods and Applications
... Superlinear convergence with q-order ˛ > 1: ke kC1 k 6 ke k k ˛ and > 0; Quadratic convergence: ke kC1 k 6 ke k k 2 and > 0. 2.5. Line search and trust region numerical methods for NLS Over the decades, ...
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Asymptotic normality of nonlinear least squares under singular experimental designs
... design points. Can we use a similar approach also for singular models? It is the purpose of this paper to present easily interpreted conditions which allow this. It is important since optimum designs may produce ...
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Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically
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General algorithms based on least squares calculations for maximum likelihood estimation in multiparameter models : a thesis presented in partial fulfilment of the requirements for the degree of Ph D in Statistics at Massey University
... to models whi ch cannot be wri tten wi th a si ngl e non-random systemati c part ...some models wi th a single nonlinear syst emati c part can be rewritten with two l inear systemati c parts and thi ...
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On the equivalence between Total Least Squares and Maximum Likelihood PCA
... The maximum likelihood PCA (MLPCA) method has been devised in chemometrics as a generalization of the well-known PCA method in order to derive consistent estimators in the presence of errors with known ...
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Deriving generalized means as least squares and maximum likelihood estimates
... generalized means can be derived in a unified way, as least squares estimates for a transformed data.. set.[r] ...
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Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models
... Nous proposons une approche unifiée pour analyser la méthode de bootstrap appliquée aux estimateurs de pseudo-maximum de vraisemblance dans le contexte de modèles non linéaires dynamiques où les données sont ...
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The Equivalence of the Maximum Likelihood and a Modified Least Squares for a Case of Generalized Linear Model
... this maximum likelihood approach is applied when the data are approximately ...linear models (GLM), Mc Cullagh and Nelder1989, and on the estimate of quasi- likelihood (QL) developed by ...
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On the Maximum Likelihood and Least Squares Estimation for the Inverse Weibull Parameters with Progressively First Failure Censoring
... the maximum likelihood, approximate maximum likelihood and the least squares method estimators for the unknown pa- rameters of the inverse Weibull distribution are ...the ...
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(ii) Study linear model for classification and its relation with least squares, maximum likelihood estimation, and maximum a posteriori estimation
... Least-Mean-Square Algorithm LMS algorithm was invented by Widrow and Hoff, and the corresponding work was published in 1960: Widrow has been a professor at Standard University. His research focuses on adaptive ...
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