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Title: Optimal designs for comparing regression models with correlated observations
Authors: Dette, Holger 
Schorning, Kirsten 
Konstantinou, Maria 
Major Field of Science: Agricultural Sciences
Field Category: Environmental Biotechnology;Other Agricultural Sciences
Keywords: Comparing regression curves;Confidence band;Correlated observations;Linear regression;Optimal design
Issue Date: 1-Sep-2017
Source: Computational Statistics and Data Analysis, 2017, vol. 113, pp. 273-286
Volume: 113
Start page: 273
End page: 286
Journal: Computational Statistics and Data Analysis 
Abstract: The problem under investigation is that of efficient statistical inference for comparing two regression curves estimated from two samples of dependent measurements. Based on a representation of the best pair of linear unbiased estimators in continuous time models as a stochastic integral, a pair of linear unbiased estimators with corresponding optimal designs for finite sample size is constructed. This pair minimises the width of the confidence band for the difference between the estimated curves in a class of linear unbiased estimators approximating the stochastic integrals and is very close to the pair of weighted least squares estimators with corresponding optimal design. Thus results readily available in the literature are extended to the case of correlated observations and an easily implementable solution is provided which is practically non distinguishable from the weighted least squares estimators. The advantages of using the proposed pairs of estimators with corresponding optimal designs for the comparison of regression models are illustrated via several numerical examples.
ISSN: 0167-9473
DOI: 10.1016/j.csda.2016.06.017
Rights: © Elsevier
Type: Article
Affiliation : Ruhr-Universität Bochum 
Cyprus University of Technology 
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