Arbeitspapier
Optimal experimental designs for inverse quadratic regression models
In this paper optimal experimental designs for inverse quadratic regression models are determined. We consider two dfferent parameterizations of the model and investigate local optimal designs with respect to the c-, D-and E-criteria, which reflect various aspects of the precision of the maximum likelihood estimator for the parameters in inverse quadratic regression models. In particular it is demonstrated that for a sufficiently large design space geometric allocation rules are optimal with respect to many optimality criteria. Moreover, in numerous cases the designs with respect to the different criteria are supported at the same points. Finally, the effiiencies of different optimal designs with respect to various optimality criteria are studied, and the effiiency of some commonly used designs are investigated.
- Sprache
-
Englisch
- Erschienen in
-
Series: Technical Report ; No. 2007,37
- Thema
-
rational regression models
optimal designs
Chebyshev systems
E-,c-,D-optimality
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Dette, Holger
Kiss, Christine
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (wo)
-
Dortmund
- (wann)
-
2007
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:46 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Dette, Holger
- Kiss, Christine
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Entstanden
- 2007
Ähnliche Objekte (12)
A unified asymptotic expansion for distributions of quadratic functionals in nonlinear regression models
Testing strict monotonicity in nonparametric regression
A power comparison between nonparametric regression tests
Estimating a convex function in nonparametric regression
Nonparametric comparison of regression curves - an empirical process approach
Testing for symmetric error distribution in nonparametric regression models
Optimal designs for estimating the slope of a regression
Constrained D- and D1-optimal designs for polynomial regression
Some methodological aspects of validation of models in nonparametric regression
Optimal designs for estimating individual coefficients in Fourier regression models
Testing for a constant coefficient of variation in nonparametric regression
A geometric characterization of c-optimal designs for heteroscedastic regression
A unified asymptotic expansion for distributions of quadratic functionals in nonlinear regression models
Testing strict monotonicity in nonparametric regression
A power comparison between nonparametric regression tests
Estimating a convex function in nonparametric regression
Nonparametric comparison of regression curves - an empirical process approach
Testing for symmetric error distribution in nonparametric regression models
Optimal designs for estimating the slope of a regression
Constrained D- and D1-optimal designs for polynomial regression
Some methodological aspects of validation of models in nonparametric regression
Optimal designs for estimating individual coefficients in Fourier regression models
Testing for a constant coefficient of variation in nonparametric regression
A geometric characterization of c-optimal designs for heteroscedastic regression
A unified asymptotic expansion for distributions of quadratic functionals in nonlinear regression models
Testing strict monotonicity in nonparametric regression
A power comparison between nonparametric regression tests
Estimating a convex function in nonparametric regression
Nonparametric comparison of regression curves - an empirical process approach
Testing for symmetric error distribution in nonparametric regression models
Optimal designs for estimating the slope of a regression
Constrained D- and D1-optimal designs for polynomial regression
Some methodological aspects of validation of models in nonparametric regression
Optimal designs for estimating individual coefficients in Fourier regression models
Testing for a constant coefficient of variation in nonparametric regression