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Accounting for intrinsic nonlinearity in nonlinear regression parameter inference regions. (English) Zbl 0537.62045

Joint confidence and likelihood regions for the parameters in nonlinear regression models can be defined using the geometric concepts of sample space and solution locus. Using a quadratic approximation to the solution locus, instead of the usual linear approximation, it is shown that these inference regions correspond to ellipsoids on the tangent plane at the least squares point (From the authors’ summary).
Reviewer: N.Leonenko

MSC:

62J02 General nonlinear regression
62F25 Parametric tolerance and confidence regions
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