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A Bayesian Factor Analysis Model with Generalized Prior Information

Rowe, Daniel B. (2000) A Bayesian Factor Analysis Model with Generalized Prior Information. Social Science Working Paper, 1099. California Institute of Technology , Pasadena, CA. (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20170807-164537348

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Abstract

In the Bayesian approach to factor analysis, available prior knowledge regarding the model parameters is quantified in the form of prior distributions and incorporated into the inferences. The incorporation of prior knowledge has the added consequence of eliminating the ambiguity of rotation found in the traditional factor analysis model. Previous Bayesian factor analysis work (Press & Shigemasu 1989, & Press 1998, Rowe 2000a, and Rowe 2000b), has considered mainly natural conjugate prior distributions for the model parameters. As is mentioned in Press (1982), Rothenburg (1963) pointed out that with a natural conjugate prior distribution, the elements in the covariance matrices are constrained and thus may not be rich enough to permit freedom of assessment. In this paper, generalized natural conjugate distributions are used to quantify and incorporate available prior information which permit complete freedom of assessment.


Item Type:Report or Paper (Working Paper)
Group:Social Science Working Papers
Series Name:Social Science Working Paper
Issue or Number:1099
Record Number:CaltechAUTHORS:20170807-164537348
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170807-164537348
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79884
Collection:CaltechAUTHORS
Deposited By: Jacquelyn Bussone
Deposited On:09 Aug 2017 19:02
Last Modified:03 Oct 2019 18:25

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