Arbeitspapier
Testing additivity by kernel based methods - what is a reasonable test?
In the common nonparametric regression model with high dimensional predictor several tests for the hypothesis of an additive regression are investigated. The corresponding test statistics are either based on the diiferences between a fit under the assumption of additivity and a fit in the general model or based on residuals under the assumption of additivity. For all tests asymptotic normality is established under the null hypothesis of additivity and under fixed alternatives with different rates of convergence corresponding to both cases. These results are used for a comparison of the different methods. It is demonstrated that a statistic based on an empirical L1 - distance of the Nadaraya Watson and the marginal integration estimator yields the asymptotically most efficient procedure if these are compared with respect to the asymptotic behaviour under fixed alternatives.
- Language
-
Englisch
- Bibliographic citation
-
Series: Technical Report ; No. 2000,39
- Subject
-
Additive models
dimension reduction
test of additivity
marginal integration estimate
- Event
-
Geistige Schöpfung
- (who)
-
Dette, Holger
von Lieres und Wilkau, Carsten
- Event
-
Veröffentlichung
- (who)
-
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
-
Dortmund
- (when)
-
2000
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Dette, Holger
- von Lieres und Wilkau, Carsten
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2000
Other Objects (12)
Testing linearity of regression models with dependent errors by kernel based methods
A comparative study of monotone nonparametric kernel estimates
A note on estimating a monotone regression by combining kernel and density estimates
Testing equality of spectral densities
Testing strict monotonicity in nonparametric regression
Testing for symmetric error distribution in nonparametric regression models
A note on testing the covariance matrix for large dimension
Testing for a constant coefficient of variation in nonparametric regression
Canonical moments, orthogonal polynomials with application to statistics
On robust and efficient designs for risk estimation in epidemiologic studies
Strong approximation of eigenvalues of large dimensional Wishart matrices by roots of generalized Laguerre polynomials
Optimal designs for testing the functional form of a regression via nonparametric estimation techniques
Testing linearity of regression models with dependent errors by kernel based methods
A comparative study of monotone nonparametric kernel estimates
A note on estimating a monotone regression by combining kernel and density estimates
Testing equality of spectral densities
Testing strict monotonicity in nonparametric regression
Testing for symmetric error distribution in nonparametric regression models
A note on testing the covariance matrix for large dimension
Testing for a constant coefficient of variation in nonparametric regression
Canonical moments, orthogonal polynomials with application to statistics
On robust and efficient designs for risk estimation in epidemiologic studies
Strong approximation of eigenvalues of large dimensional Wishart matrices by roots of generalized Laguerre polynomials
Optimal designs for testing the functional form of a regression via nonparametric estimation techniques
Testing linearity of regression models with dependent errors by kernel based methods
A comparative study of monotone nonparametric kernel estimates
A note on estimating a monotone regression by combining kernel and density estimates
Testing equality of spectral densities
Testing strict monotonicity in nonparametric regression
Testing for symmetric error distribution in nonparametric regression models
A note on testing the covariance matrix for large dimension
Testing for a constant coefficient of variation in nonparametric regression
Canonical moments, orthogonal polynomials with application to statistics
On robust and efficient designs for risk estimation in epidemiologic studies
Strong approximation of eigenvalues of large dimensional Wishart matrices by roots of generalized Laguerre polynomials