Similar Search Results

Asymptotic Optimality of Generalized C_{L}, Cross-Validation, and Generalized Cross-Validation in Regression with Heteroskedastic Errors

Andrews, Donald W.K. - Cowles Foundation for Research in Economics, Yale University - 1989

The problem considered here is that of using a data-driven procedure to select a good estimate from a class of linear estimates indexed by a discrete parameter. In contrast to other papers on this subject, we consider models with heteroskedastic errors. The results apply to model selection...

Persistent link: https://www.econbiz.de/10005634704

Empirical studies in a multivariate non-stationary, nonparametric regression model for financial returns

Gürtler, Marc; Rauh, Ronald - 2013

In this paper we analyze a multivariate non-stationary regression model empirically. With the knowledge about unconditional heteroscedasticty of financial returns, based on univariate studies and a congruent paradigm in Gürtler and Rauh (2009), we test for a time-varying covariance structure...

Persistent link: https://www.econbiz.de/10010311041

Empirical studies in a multivariate non-stationary, nonparametric regression model for financial returns

Gürtler, Marc; Rauh, Ronald - Department Wirtschaftswissenschaften, Technische … - 2013

Persistent link: https://www.econbiz.de/10010985506

Convergence rates of general regularization methods for statistical inverse problems and applications

Bissantz, Nicolai; Hohage, T.; Munk, Axel; Ruymgaart, F. - 2007

error of a large class of regularization methods (spectral methods) including the aforementioned estimators as well as many … precise convergence rates for satellite gradiometry, L2-boosting, and errors in variable problems. …

Persistent link: https://www.econbiz.de/10010298188

Convergence rates of general regularization methods for statistical inverse problems and applications

Bissantz, Nicolai; Hohage, T.; Munk, Axel; Ruymgaart, F. - Institut für Wirtschafts- und Sozialstatistik, … - 2007

Persistent link: https://www.econbiz.de/10009219813

Nonparametric and semiparametric regression model selection

Gao, Jiti; Tong, Howell - Volkswirtschaftliche Fakultät, … - 2002

It is known that semiparametric time series regression is often used without checking its suitability and compactness. In theory, this may result in dealing with an unnecessarily complicated model. In practice, one may encounter the computational difficulty caused by the spareness of the data....

Persistent link: https://www.econbiz.de/10005621775

Efficiency of the V-fold model selection for localized bases

Navarro, Fabien; Saumard, Adrien - 2017

Persistent link: https://www.econbiz.de/10012200011

On robustness of model-based bootstrap schemes in nonparametric time series analysis

Neumann, Michael H. - 1997

asymptotically for the bootstrap to be valid. However, certain model-based bootstrap methods remain valid for some interesting … bootstrap schemes remain valid for supremum-type functionals as long as they mimic the corresponding finite-dimensional joint … distributions consistently. As an example, we investigate a finite order Markov chain bootstrap in the context of a general …

Persistent link: https://www.econbiz.de/10010310822

A simple test for the parametric form of the variance function in nonparametric regression

Dette, Holger; Hetzler, Benjamin - 2006

In this paper a new test for the parametric form of the variance function in the common nonparametric regression model is proposed which is applicable under very weak smoothness assumptions. The new test is based on an empirical process formed from pseudo residuals, for which weak convergence to...

Persistent link: https://www.econbiz.de/10010296720

Goodness-of-Fit Tests in Nonparametric Regression

Einmahl, John; van Keilegom, I. - Tilburg University, Center for Economic Research - 2006

AMS classifications: 62G08, 62G10, 62G20, 62G30; 60F17.

Persistent link: https://www.econbiz.de/10011091096