Critical appraisal of jointness concepts in Bayesian model averaging: evidence from life sciences,sociology, and other scientific fields

Jointness is a Bayesian approach to capturing dependence among regressors in multivariate data. It addresses the general issue of whether explanatory factors for a given empirical phenomenon are complements or substitutes. I ask a number of questions about existing jointness concepts: Are the patterns revealed stable across datasets? Are results robust to prior choice and do data characteristics affect results? And importantly: What do the answers imply from a practical vista? The present study takes an applied, interdisciplinary and comparative perspective, validating jointness concepts on datasets across scientific fields with focus on life sciences (Parkinson's disease) and sociology. Simulations complement the study of real-world data. My findings suggest that results depend on which jointness concept is used: Some concepts deliver jointness patterns remarkably uniform across datasets, while all concepts are fairly robust to the choice of prior structure. This can be interpreted as critique of jointness from a practical perspective, given that the patterns revealed are at times very different and no concept emerges as overall advantageous. The composite indicators approach to combining information across jointness concepts is also explored, suggesting an avenue to facilitate the application of the concepts in future research.