Bayesian factor analysis offers a probabilistic framework for uncovering latent structure in datasets where the number of observed variables greatly exceeds the sample size. By positing that ...

Purpose The latent structure of the Hospital Anxiety and Depression Scale (HADS) has caused inconsistent results in the literature. The HADS is frequently analyzed via maximum likelihood confirmatory ...

When estimating DSGE models, the number of observable economic variables is usually kept small, and it is conveniently assumed that DSGE model variables are perfectly measured by a single data series.

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about ...

New FDA guidance on the use of Bayesian statistics signals a broader shift in accommodating more flexible clinical trial ...