Abstract
Bayesian Factor Analysis was introduced by Press and Shigemasu (1989) (PS). For Bayesian analysis, the prior distribution permits the analyst to identify the model by bringing prior information about the model to bear. In PS, it was assumed that the parameters of the prior distribution, the hyperparameters, were known, or would be readily assessable from a preliminary principal components analysis. In this study, we provide an empirical Bayes (EB) way of assessing the hyperparameters from the current data, and we show, using the AIC and BIC criteria, that it improves upon the suggested assessment method of the PS model.
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References
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© 2008 Birkhäuser Boston
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Press, S.J., Shin, KI., Eun, L.S. (2008). Empirical Bayes Assessment of the Hyperparameters in Bayesian. In: Advances in Mathematical and Statistical Modeling. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4626-4_6
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DOI: https://doi.org/10.1007/978-0-8176-4626-4_6
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Online ISBN: 978-0-8176-4626-4
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