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Analysis of covariance under variance heteroscedasticity in general factorial designs
Author(s) -
Konietschke Frank,
Cao Cong,
Gunawardana Asanka,
Zimmermann Georg
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9092
Subject(s) - homoscedasticity , heteroscedasticity , statistics , analysis of covariance , covariate , mathematics , covariance , sample size determination , econometrics , inference , statistical hypothesis testing , analysis of variance , statistical inference , null hypothesis , computer science , artificial intelligence
Adjusting for baseline values and covariates is a recurrent statistical problem in medical science. In particular, variance heteroscedasticity is non‐negligible in experimental designs and ignoring it might result in false conclusions. Approximate inference methods are developed to test null hypotheses formulated in terms of adjusted treatment effects and regression parameters in general analysis of covariance designs with arbitrary numbers of factors. Variance homoscedasticity is not assumed. The distributions of the test statistics are approximated using Box‐type approximation methods. Extensive simulation studies show that the procedures are particularly suitable when sample sizes are rather small. A real data set illustrates the application of the methods.