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  1. Bayesian (non-)unique sparse factor modelling
    Author: Kaufmann, Sylvia; Pape, Markus
    Published: [2023]
    Publisher:  [Study Center Gerzensee], [Gerzensee]

    Factor modelling extracts common information from a high-dimensional data set into few common components, where the latent factors usually explain a large share of data variation. Exploratory factor estimation induces sparsity into the loading matrix... more

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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 529
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    No inter-library loan

     

    Factor modelling extracts common information from a high-dimensional data set into few common components, where the latent factors usually explain a large share of data variation. Exploratory factor estimation induces sparsity into the loading matrix to associate units or series with those factors most strongly associated with them, eventually determining factor interpretation. We motivate geometrically under which circumstances it may be necessary to consider the existence of multiple sparse factor loading matrices with similar degrees of sparsity for a given data set. We propose two MCMC approaches for Bayesian inference and corresponding post-processing algorithms to uncover multiple sparse representations of the factor loadings matrix. We investigate both approaches in a simulation study. Applied to data on country-specific gross domestic product and U.S. price components series, we retrieve multiple sparse factor representations for each data set. Both approaches prove useful to discriminate between pervasive and weaker factors.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/283524
    Series: Working paper / Study Center Gerzensee ; 23, 04
    Subjects: Multimodality; Sparsity; Pervasive and weak factors
    Scope: 1 Online-Ressource (circa 49 Seiten), Illustrationen