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Statistical Tools for Nonlinear Regression
1996International ...
Huet, S. +3 more
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Heteroscedastic Nonlinear Regression Models
Communications in Statistics - Simulation and Computation, 2010In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm,
Edilberto Cepeda Cuervo +1 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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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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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.
openaire +1 more source