Arbeitspapier
A note on all-bias designs with applications in spline regression models
If a model is fitted to empirical data, bias can arise from terms which are not incorporated in the model assumptions. As a consequence the commonly used optimality criteria based on the generalized variance of the estimate of the model parameters may not lead to efficient designs for the statistical analysis. In this note some general aspects of all-bias designs are presented, which were introduced in this context by Box and Draper (1959). We establish sufficient conditions such that a given design is an all-bias design and illustrate these in the special case of spline regression models. In particular our results generalize recent findings of Woods and Lewis (2006).
- Sprache
-
Englisch
- Erschienen in
-
Series: Technical Report ; No. 2008,19
- Thema
-
all-bias design
spline regression models
robust designs
quadrature formulas
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Dette, Holger
Melas, Viatcheslav B.
- Ereignis
-
Veröffentlichung
- (wer)
-
Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (wo)
-
Dortmund
- (wann)
-
2008
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 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
- Melas, Viatcheslav B.
- Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Entstanden
- 2008
Ähnliche Objekte (12)
Theoretical and practical contributions to Bayesian penalized spline regression
Bias in nearest-neighbor hazard estimation
Testing strict monotonicity in nonparametric regression
A power comparison between nonparametric regression tests
Estimating a convex function in nonparametric regression
Optimal experimental designs for inverse quadratic regression models
Testing for symmetric error distribution in nonparametric regression models
Nonparametric comparison of regression curves - an empirical process approach
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
Theoretical and practical contributions to Bayesian penalized spline regression
Bias in nearest-neighbor hazard estimation
Testing strict monotonicity in nonparametric regression
A power comparison between nonparametric regression tests
Estimating a convex function in nonparametric regression
Optimal experimental designs for inverse quadratic regression models
Testing for symmetric error distribution in nonparametric regression models
Nonparametric comparison of regression curves - an empirical process approach
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
Theoretical and practical contributions to Bayesian penalized spline regression
Bias in nearest-neighbor hazard estimation
Testing strict monotonicity in nonparametric regression
A power comparison between nonparametric regression tests
Estimating a convex function in nonparametric regression
Optimal experimental designs for inverse quadratic regression models
Testing for symmetric error distribution in nonparametric regression models
Nonparametric comparison of regression curves - an empirical process approach
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