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A Continuation Method for Nonlinear Regression
SIAM Journal on Numerical Analysis, 1981Many iterative regression methods only perform well near a solution.One may construct a modified problem with a free parameter $(k)$, which has a known solution for a particular k. By suitably deforming this problem through k-space one may, always remaining close to an intermediate solution, eventually solve the original problem.The continuation method
De Villiers, Noel, Glasser, David
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On nonlinear beta regression residuals
Biometrical Journal, 2017We proposed a new residual to be used in linear and nonlinear beta regressions. Unlike the residuals that had already been proposed, the derivation of the new residual takes into account not only information relative to the estimation of the mean submodel but also takes into account information obtained from the precision submodel. This is an advantage
Espinheira, Patrícia L. +2 more
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Nonlinear regression using spreadsheets
Trends in Pharmacological Sciences, 1995Pharmacologists are often required to analyse nonlinear experimental effects by fitting the data to defined theoretical models. This may require a specialized computer program capable of performing nonlinear regression analysis, which can prove costly given the variety of pharmacological research.
W P, Bowen, J C, Jerman
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Nonlinear Regression With Variance Components
Journal of the American Statistical Association, 1992Abstract The nonlinear model with variance components, which combines a nonlinear model for the mean with additive random effects, is applicable to split-plot and nested experiments. We propose two methods of estimation for the parameters of the nonlinear model for the mean: (1) estimated generalized least squares (EGLS), and (2) maximum likelihood ...
Marcia L. Gumpertz, Sastry G. Pantula
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Statistical Tools for Nonlinear Regression
1996International ...
Huet, S. +3 more
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Nonstationary nonlinear quantile regression
Econometric Reviews, 2016ABSTRACTThis study examines estimation and inference based on quantile regression for parametric nonlinear models with an integrated time series covariate. We first derive the limiting distribution of the nonlinear quantile regression estimator and then consider testing for parameter restrictions, when the regression function is specified as an ...
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Prediction Intervals in Nonlinear Regression
Biometrical Journal, 1991AbstractBy treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θ, we construct two‐sided nominally‐q‐prediction intervals by applying the usual linear model theory. The derivation of the truncated series expansion of the expected coverage of the prediction intervals at a ...
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2006
The basic idea of nonlinear regression is the same as that of linear regression, namely to relate a response to a vector of predictor variables. Nonlinear regression is characterized by the fact that the prediction equation depends nonlinearly on one or more unknown parameters.
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The basic idea of nonlinear regression is the same as that of linear regression, namely to relate a response to a vector of predictor variables. Nonlinear regression is characterized by the fact that the prediction equation depends nonlinearly on one or more unknown parameters.
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Asymmetry of estimators in nonlinear regression
Biometrika, 1987The distributions of estimators \({\hat \vartheta}\) of the parameter \(\vartheta\) of intrinsically nonlinear regression functions f(x,\(\vartheta)\) are unknown but even with normally distributed errors they are known to be skew. A measure of asymmetry \(\lambda\) (i) of the distribution of the i-th component \({\hat \vartheta}\)(i) of \({\hat ...
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