Optimal designs for comparing regression models with correlated observations
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Date
2016
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Abstract
We consider the problem 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, an efficient 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. We thus extend results readily
available in the literature to the case of correlated observations and provide an easily
implementable and efficient solution. The advantages of using such pairs of estimators
with corresponding optimal designs for the comparison of regression models are illustrated
via numerical examples.
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Keywords
linear regression, optimal design, confidence band, comparing regression curves, correlated observations