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.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 8338
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Computational Techniques; Simulation Modeling
- Thema
-
Bayesian factor models
exploratory factor analysis
identifiability
marginal data augmentation
model expansion
model selection
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Conti, Gabriella
Frühwirth-Schnatter, Sylvia
Heckman, James J.
Piatek, Rémi
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute for the Study of Labor (IZA)
- (wo)
-
Bonn
- (wann)
-
2014
- Handle
- Letzte Aktualisierung
-
2025-03-10T11:42:47+0100
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Conti, Gabriella
- Frühwirth-Schnatter, Sylvia
- Heckman, James J.
- Piatek, Rémi
- Institute for the Study of Labor (IZA)
Entstanden
- 2014
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