Geodesic
Underparametrization of weakly nonlinear regression models
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica, Tome 49 (2010) no. 2, pp. 83-93 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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A large number of parameters in regression models can be serious obstacle for processing and interpretation of experimental data. One way how to overcome it is an elimination of some parameters. In some cases it need not deteriorate statistical properties of estimators of useful parameters and can help to interpret them. The problem is to find conditions which enable us to decide whether such favourable situation occurs.
A large number of parameters in regression models can be serious obstacle for processing and interpretation of experimental data. One way how to overcome it is an elimination of some parameters. In some cases it need not deteriorate statistical properties of estimators of useful parameters and can help to interpret them. The problem is to find conditions which enable us to decide whether such favourable situation occurs.
Classification : 62F10, 62J05
Keywords: Weakly nonlinear regression model; underparameterization; MSE; BLUE 
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Kubáček, Lubomír; Tesaříková, Eva. Underparametrization of weakly nonlinear regression models. Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica, Tome 49 (2010) no. 2, pp. 83-93. http://geodesic.mathdoc.fr/item/AUPO_2010_49_2_a6/

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