Hybrid least-squares algorithm MINOPT for a nonlinear regression is introduced. MINOPT from CHEMSTAT package combines fast convergence of the Gauss-Newton method in a vicinity of minimum with good convergence of gradient methods for location far from a minimum. Quality of minimization and an accuracy of parameter estimates for six selected models are examined and compared with different derivative least-squares methods of five commercial regression packages
Algorithm for the exact solution of the problem of estimating the parameters of linear regression mo...
This contribution contains a description and analysis of effective methods for minimization of the n...
Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does,...
Estimation of nonlinear regression quality leads to examination of quality of parameter estimates, a...
summary:A numerical method of fitting a multiparameter function, non-linear in the parameters which ...
Seven computerprograms for non-linear regression or curve fitting problems are compared. The compari...
<p/>Nonlinear parameter optimization in least squares was studied from a point of view of diff...
The least square method is analyzed. The basic aspects of the method are discussed. Emphasis is give...
The problem of optimizing a nonlinear function of one or more variables in the sense of locating the...
The purpose of this thesis is to examine different methods of curve-fitting through the process of n...
The conditional, unconditional, or the exact maximum likelihood estimation and the least-squares est...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...
The work covers the statistical experiments generated by the non-linear regression models. The iam i...
A differential correction technique for solving nonlinear minimax problems is presented. The basis o...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
Algorithm for the exact solution of the problem of estimating the parameters of linear regression mo...
This contribution contains a description and analysis of effective methods for minimization of the n...
Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does,...
Estimation of nonlinear regression quality leads to examination of quality of parameter estimates, a...
summary:A numerical method of fitting a multiparameter function, non-linear in the parameters which ...
Seven computerprograms for non-linear regression or curve fitting problems are compared. The compari...
<p/>Nonlinear parameter optimization in least squares was studied from a point of view of diff...
The least square method is analyzed. The basic aspects of the method are discussed. Emphasis is give...
The problem of optimizing a nonlinear function of one or more variables in the sense of locating the...
The purpose of this thesis is to examine different methods of curve-fitting through the process of n...
The conditional, unconditional, or the exact maximum likelihood estimation and the least-squares est...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...
The work covers the statistical experiments generated by the non-linear regression models. The iam i...
A differential correction technique for solving nonlinear minimax problems is presented. The basis o...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
Algorithm for the exact solution of the problem of estimating the parameters of linear regression mo...
This contribution contains a description and analysis of effective methods for minimization of the n...
Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does,...