论文信息 - Least Squares Estimation of Parameters in Implicit Models

Least Squares Estimation of Parameters in Implicit Models

Abstract This paper is concerned with the development, implementation and testing of an iterative method for the weighted least squares parameter estimation in implicit models, when all experimental data are subject to random errors. The algorithm does not require any linearization of model or normal equations and provides the least squares solution including the variance–covariance matrix of the parameters. Two examples are included in order to test the method and demonstrate the main aspects of its application.

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