Maximum likelihood estimation with nonlinear regression in polarographic and potentiometric studies

In this work we review some aspects of maximum likelihood nonlinear modelin g in polarographic and potentiometric techniques. Different algorithms, nam ely the Levenberg-Marquardt and the "error-in-variables'' methods in parame tric and Monte-Carlo nonparametric estimation are used. Conclusions are dra wn upon the influence of experimental errors and error correlation, introdu ced via statistical weighting, in the accuracy and precision of the estimat ed parameters. Several of the tested alternatives, including regression on the signal variable alone with a global error weighting function, are shown to provide adequate representation of the experimental data. (C) 2001 Else vier Science B.V. All rights reserved.