Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/317995 
Year of Publication: 
2023
Citation: 
[Journal:] AStA Advances in Statistical Analysis [ISSN:] 1863-818X [Volume:] 108 [Issue:] 3 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2023 [Pages:] 577-609
Publisher: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Abstract: 
Abstract Orthonormality constraints are common in reduced rank models. They imply that matrix-variate parameters are given as orthonormal column vectors. However, these orthonormality restrictions do not provide identification for all parameters. For this setup, we show how the remaining identification issue can be handled in a Bayesian analysis via post-processing the sampling output according to an appropriately specified loss function. This extends the possibilities for Bayesian inference in reduced rank regression models with a part of the parameter space restricted to the Stiefel manifold. Besides inference, we also discuss model selection in terms of posterior predictive assessment. We illustrate the proposed approach with a simulation study and an empirical application.
Subjects: 
Bayesian estimation
Post-processing
Reduced rank regression
Orthogonal transformation
Model selection
Stiefel manifold
Posterior predictive assessment
Persistent Identifier of the first edition: 
Additional Information: 
C11;C31;C51;C52
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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