Two possible applications of nonlinear regression models in insurance are discussed. The first part deals withmodelling IBNR reserves when a cubic approximation to the solution locus is used instead of linear or quadratic ones. Aformula is given for construction of improved confidence regions for parameters in such models.Using this approachIBNR reserves for a data set are computed.In the second part a method is proposed of how to measure the influence ofadditive perturbations on nonlinear regression model parameters. An example is given which shows how this method canbe used to preserve privacy of sensitive data in insurance business.
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