A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied
The work covers the statistical experiments generated by the non-linear regression models. The iam i...
This report contains a detailed theoretical description of an all-purpose, interactive curve-fitting...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...
The method of least squares is used to come up with a regression trend equation of a nonlinear expon...
This thesis is concerned with the estimation of the nonlinear parameters in statistical models consi...
Simple methods are presented for determining estimators to be used at the first stage of a nonlinear...
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald ...
A wide variety of chemical and biophysical processes are describable in a nonlinear function consist...
Linear analog operation followed by nonlinear digital signal processing for nonlinear parameter appr...
Regression techniques are developed for batch estimation and applied to three specific areas, namely...
En esta primera parte se revisan las técnicas comúnmente usadas en la determinación de parámetros de...
Systematic investigation of mathematical models by comparison of experimental and analytical regress...
Curve fitting for modified paired comparisons using linearized nonlinear regressio
Linear regression models are useful for estimating statistical relationship between related variable...
summary:A numerical method of fitting a multiparameter function, non-linear in the parameters which ...
The work covers the statistical experiments generated by the non-linear regression models. The iam i...
This report contains a detailed theoretical description of an all-purpose, interactive curve-fitting...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...
The method of least squares is used to come up with a regression trend equation of a nonlinear expon...
This thesis is concerned with the estimation of the nonlinear parameters in statistical models consi...
Simple methods are presented for determining estimators to be used at the first stage of a nonlinear...
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald ...
A wide variety of chemical and biophysical processes are describable in a nonlinear function consist...
Linear analog operation followed by nonlinear digital signal processing for nonlinear parameter appr...
Regression techniques are developed for batch estimation and applied to three specific areas, namely...
En esta primera parte se revisan las técnicas comúnmente usadas en la determinación de parámetros de...
Systematic investigation of mathematical models by comparison of experimental and analytical regress...
Curve fitting for modified paired comparisons using linearized nonlinear regressio
Linear regression models are useful for estimating statistical relationship between related variable...
summary:A numerical method of fitting a multiparameter function, non-linear in the parameters which ...
The work covers the statistical experiments generated by the non-linear regression models. The iam i...
This report contains a detailed theoretical description of an all-purpose, interactive curve-fitting...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...