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Optimal Experimental Design and Accuracy of Parameter Estimation for Nonlinear Regression Models Used in Long‐term Selection
Author(s) -
Rudolph P. E.,
Herrendörfer G.
Publication year - 1995
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710370209
Subject(s) - nonlinear regression , term (time) , parametric statistics , mathematics , selection (genetic algorithm) , estimator , nonlinear system , regression analysis , statistics , interval (graph theory) , model selection , estimation theory , regression , exponential function , parametric model , computer science , machine learning , mathematical analysis , physics , quantum mechanics , combinatorics
An experimental design problem is considered for the analysis of long‐term selection experiments with nonlinear regression models. For a 3‐parametric exponential regression function whose parameters have also a reasonable biological interpretation approximate formulas for the determination of the necessary number of observations at each generation are constructed in such a way that the half expected length of an (1 — α)‐confidence interval for a chosen parameter is not greater than a given value. In this sense the accuracy of the parameter estimators can be described.