Arbeitspapier
Bayesian Exploratory Factor Analysis
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.
- Language
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Englisch
- Bibliographic citation
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Series: NRN Working Paper, NRN: The Austrian Center for Labor Economics and the Analysis of the Welfare State ; No. 1408
- Classification
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Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Computational Techniques; Simulation Modeling
- Subject
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Bayesian Factor Models
Exploratory Factor Analysis
Identifiability
Marginal Data Augmentation
Model Expansion
Model Selection
- Event
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Geistige Schöpfung
- (who)
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Conti, Gabriella
Frühwirth-Schnatter, Sylvia
Heckman, James J.
Piatek, Rémi
- Event
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Veröffentlichung
- (who)
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Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State
- (where)
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Linz
- (when)
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2014
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Conti, Gabriella
- Frühwirth-Schnatter, Sylvia
- Heckman, James J.
- Piatek, Rémi
- Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State
Time of origin
- 2014