Authors: Dette, Holger
Schorning, Kirsten
Konstantinou, Maria
Title: Optimal designs for series estimation in nonparametric regression with correlated data
Language (ISO): en
Abstract: In 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.
Subject Headings: optimal design
optimal estimator
integrated mean squared error
nonparametric regression
Issue Date: 2018
Appears in Collections:Sonderforschungsbereich (SFB) 823

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