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Sailer, Oliver
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Technical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2009,01
The analysis of crossover designs assuming i.i.d. errors leads to biased variance estimates whenever the true covariance structure is not spherical. As a result, the OLS F-Test for treatment differences is not valid. Bellavance et al. (Biometrics 52:607-612, 1996) use simulations to show that a modified F-Test based on an estimate of the within subjects covariance matrix allows for nearly unbiased tests. Kunert and Utzig (JRSS B 55:919-927, 1993) propose an alternative test that does not need an estimate of the covariance matrix. However, for designs with more than three observations per subject Kunert and Utzig (1993) only give a rough upper bound for the worst-case variance bias. This may lead to overly conservative tests. In this paper we derive an exact upper limit for the variance bias due to carry-over for an arbitrary number of observations per subject. The result holds for a certain class of highly efficient carry-over balanced designs.
correlated errors
crossover designs
fixed effects model
upper limit
variance estimation
Document Type: 
Working Paper

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