Parsimonious Bayesian Factor Analysis when the Number of Factors is Unknown

Beschreibung

We introduce a new and general set of identifiability conditions for factor models which handles the ordering problem associated with current common practice. In addition, the
new class of parsimonious Bayesian factor analysis leads to a factor loading matrix representation
which is an intuitive and easy to implement factor selection scheme. We argue that
the structuring the factor loadings matrix is in concordance with recent trends in applied
factor analysis. Our MCMC scheme for posterior inference makes several improvements
over the existing alternatives while outlining various strategies for conditional posterior inference
in a factor selection scenario. Four applications, two based on synthetic data and two based on well known real data, are introduced to illustrate the applicability and generality
of the new class of parsimonious factor models, as well as to highlight features of the
proposed sampling schemes.

Zeitraum	26 Apr. 2011
Ereignistitel	Booth School of Business, University of Chicago, Econometrics and Statistics Colloquium
Veranstaltungstyp	Keine Angaben
Bekanntheitsgrad	International