Add to ORKGA frequently encountered problem is the fitting of a data-vector by means of a model function with a number of parameters, whose values are unknown. Minimizing the residual sum of squares delivers the least squares estimates. The problem of their precision is only solved in the linear case. We discuss and illustrate the quality of approximate (though asymptotically exact) confidence statements
More than thirty years ago Halbert White inaugurated a “modelrobust” form of statistical inference b...
summary:An approximate value of a parameter in a nonlinear regression model is known in many cases. ...
Many phenomena in biology, chemistry, physics, and engineering are modeled by a system of possibly n...
A frequently encountered problem is the fitting of a data-vector by means of a model function with a...
An important problem in applied statistics is fitting a given model function f(fJ) with unknown para...
In nonlinear regression statistical analysis based upon interpretation of the parameter estimates ma...
The paper examines the robustness of the size and power properties of the standard non-linearity tes...
The Ph.D. thesis, called Testing for Non-linearity and Asymmetry in Time Series, focuses on various...
Within this PhD research the focus was on estimation and inference method for economic panel data th...
This article presents the results of performing the linear and nonlinear used as benchmarks by the N...
The Ph.D. thesis, called Testing for Non-linearity and Asymmetry in Time Series, focuses on various ...
Linearity tests against smooth transition nonlinearity are typically based on the standard least-squ...
Regression models are routinely used in many applied sciences for describing the relationship betwee...
Estimation of nonlinear regression quality leads to examination of quality of parameter estimates, a...
International audienceIt is well known that in non-linear estimation problems the ML estimator exhib...
More than thirty years ago Halbert White inaugurated a “modelrobust” form of statistical inference b...
summary:An approximate value of a parameter in a nonlinear regression model is known in many cases. ...
Many phenomena in biology, chemistry, physics, and engineering are modeled by a system of possibly n...
A frequently encountered problem is the fitting of a data-vector by means of a model function with a...
An important problem in applied statistics is fitting a given model function f(fJ) with unknown para...
In nonlinear regression statistical analysis based upon interpretation of the parameter estimates ma...
The paper examines the robustness of the size and power properties of the standard non-linearity tes...
The Ph.D. thesis, called Testing for Non-linearity and Asymmetry in Time Series, focuses on various...
Within this PhD research the focus was on estimation and inference method for economic panel data th...
This article presents the results of performing the linear and nonlinear used as benchmarks by the N...
The Ph.D. thesis, called Testing for Non-linearity and Asymmetry in Time Series, focuses on various ...
Linearity tests against smooth transition nonlinearity are typically based on the standard least-squ...
Regression models are routinely used in many applied sciences for describing the relationship betwee...
Estimation of nonlinear regression quality leads to examination of quality of parameter estimates, a...
International audienceIt is well known that in non-linear estimation problems the ML estimator exhib...
More than thirty years ago Halbert White inaugurated a “modelrobust” form of statistical inference b...
summary:An approximate value of a parameter in a nonlinear regression model is known in many cases. ...
Many phenomena in biology, chemistry, physics, and engineering are modeled by a system of possibly n...