Sinopsis
Introduction. 1. Linear regression models. 2. Linear methods in nonlinear regression models. 3. Univariate regression models. 4. The structure of a multivariate nonlinear regression model and properties of L2 estimators. 5. Nonlinear regression models: computation of estimators and curvatures. 6. Local approximations of probability densities and moments of estimators. 7. Global approximations of densities of L2 estimators. 8. Statistical consequences of global approximations especially in flat models. 9. Nonlinear exponential families. References. Basic symbols. Subject index.
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