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Keywords:
nonlinear regression model; linearization; parameter effect curvature; intrinsic curvature
nonlinear regression model; linearization; parameter effect curvature; intrinsic curvature
Summary:
An approximate value of a parameter in a nonlinear regression model is known in many cases. In such situation a linearization of the model is possible however it is important to recognize, whether the difference between the actual value of the parameter and the approximate value does not cause significant changes, e.g., in the bias of the estimator or in its variance, etc. Some rules suitable for a solution of this problem are given in the paper.
References:
[1] Bates, D. M. and Watts, D. G.: Relative curvature measures of nonlinearity. J. Roy. Stat. Soc. B 42 (1980), 1–25. MR 0567196
[2] Janko,J.: Statistické tabulky. Praha, Academia, 1958. MR 0150924
[3] Pázman, A.: Nonlinear Statistical Models. Kluwer Academic Publishers, Dordrecht-Boston-London and Ister Science Press, Bratislava, 1993. MR 1254661
[4] Potocký, R., To Van Ban: Confidence regions in nonlinear regression models. Appl. of Math. 37 (1992), 29–39. MR 1152155
[5] Rao, C.R.: Linear Statistical Inference and Its Applications (2nd Edition). J. Wiley, New York, 1973. MR 0346957
[6] Scheffé, H.: The Analysis of Variance (fifth printing). J. Wiley, New York-London-Sydney, 1967. MR 1673563
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