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A generalization of the Gauss-Newton method that solves extended least-squares problems

Abstract
Modeling the mean of a random variable as a function of unknown parameters leads to a nonlinear least-squares objective function. The Gauss-Newton method reduces nonlinear least-squares problems to a sequence of linear least-squares problems and requires only first-order information about the model functions. In a more general heteroscedastic setting, there are also unknown parameters in a model for the variance. This leads to an objective function that is no longer a sum of squares. We present an extension of the Gauss-Newton method that minimizes this objective function by reducing the problem to a sequence of linear least-squares problems. The extension requires only first-order information. This represents a new result because other methods that reduce this problem to a sequence of linear least-squares problems do not necessarily converge.

Paper
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