Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/317995 
Erscheinungsjahr: 
2023
Quellenangabe: 
[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
Verlag: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Bayesian estimation
Post-processing
Reduced rank regression
Orthogonal transformation
Model selection
Stiefel manifold
Posterior predictive assessment
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Sonstige Angaben: 
C11;C31;C51;C52
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