Optimal designs for series estimation in nonparametric regression with correlated data

Simple item page
dc.contributor.authorDette, Holger
dc.contributor.authorSchorning, Kirsten
dc.contributor.authorKonstantinou, Maria
dc.date.accessioned2018-12-14T14:05:07Z
dc.date.available2018-12-14T14:05:07Z
dc.date.issued2018
dc.description.abstractIn this paper we investigate the problem of designing experiments for series estimators in nonparametric regression models with correlated observations. We use projection based estimators to derive an explicit solution of the best linear oracle estimator in the continuous time model for all Markovian-type error processes. These solutions are then used to construct estimators, which can be calculated from the available data along with their corresponding optimal design points. Our results are illustrated by means of a simulation study, which demonstrates that the new series estimator has a better performance than the commonly used techniques based on the optimal linear unbiased estimators. Moreover, we show that the performance of the estimators proposed in this paper can be further improved by choosing the design points appropriately.en
dc.identifier.urihttp://hdl.handle.net/2003/37836
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-19831
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;35/2018en
dc.subjectoptimal designen
dc.subjectoptimal estimatoren
dc.subjectintegrated mean squared erroren
dc.subjectnonparametric regressionen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleOptimal designs for series estimation in nonparametric regression with correlated dataen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
DP_3518_SFB823_Dette_Schorning_Konstantinou.pdf
Size:
374.75 KB
Format:
Adobe Portable Document Format
Description:
DNB
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.85 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Sonderforschungsbereich (SFB) 823