A discussion of 'Sparse Bayesian factor analysis when the number of factors is unknown' by Sylvia Fruhwirth-Schnatter, Darjus Hosszejni and Hedibert Freitas Lopes

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Abstract

Conditional on knowing the number of factors, r, analysis in static and dynamic factor models is straightforward for the Bayesian. However, inference on r is challenging. A Bayesian could use marginal likelihoods to select the number of factors (see Geweke, 1996). But in the standard big data setups nowadays (which involve a large number of variables/measurements m), this is computationally cumbersome, requiring the estimation of a large set of models that vary in r
(≤ m).
Original languageEnglish
Number of pages8
JournalBayesian Analysis
Publication statusAccepted/In press - 4 Jul 2024

Keywords

  • Bayesian analysis
  • Bayesian factor analysis
  • identification
  • prior choice
  • computation

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