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On Estimating the Fisher Information Matrix in Nonlinear Regression Models

Ch. E. Minder, G. Gillmann

Abstract


Estimation problems in nonlinear regression situations are often most easily attacked using an iterative estimation procedure for the nonlinear parameters of the regression equation and re-estimating the linear parameters at each step of the iteration. This paper proposes a method to compute an estimate of the full observed Fisher information matrix for all
parameters when using this approach for parameter estimation.

Keywords


Asymptotic covariance; Fisher information; least squares; nonlinear regression; separable models.

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