Bayes Factors for Testing Order Constraints on Variances of Dependent Outcomes

• Published in: The American statistician Vol. 75; no. 2; pp. 152 - 161
• Main Authors: Böing-Messing, Florian, Mulder, Joris
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 10.05.2021; American Statistical Association
• Subjects: Between-subjects design; Constraints; Covariance matrix; Encompassing prior; Heteroscedasticity; Hypotheses; Inequality constraint; Mixed model; Multivariate regression; Normal distribution; Regression analysis; Repeated measures; Statistical analysis; Statistical methods; Statistics; Within-subjects design
• ISSN: 0003-1305; 1537-2731
• DOI: 10.1080/00031305.2020.1715257

In statistical practice, researchers commonly focus on patterns in the means of multiple dependent outcomes while treating variances as nuisance parameters. However, in fact, there are often substantive reasons to expect certain patterns in the variances of dependent outcomes as well. For example, in a repeated measures study, one may expect the variance of the outcome to increase over time if the difference between subjects becomes more pronounced over time because the subjects respond differently to a given treatment. Such expectations can be formulated as order constrained hypotheses on the variances of the dependent outcomes. Currently, however, no methods exist for testing such hypotheses in a direct manner. To fill this gap, we develop a Bayes factor for this challenging testing problem. Our Bayes factor is based on the multivariate normal distribution with an unstructured covariance matrix, which is often used to model dependent outcomes. Order constrained hypotheses can then be formulated on the variances on the diagonal of the covariance matrix. To compute Bayes factors between multiple order constrained hypotheses, a prior distribution needs to be specified under every hypothesis to be tested. Here, we use the encompassing prior approach in which priors under order constrained hypotheses are truncations of the prior under the unconstrained hypothesis. The resulting Bayes factor is fully automatic in the sense that no subjective priors need to be specified by the user.