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DTSTART:20070311T020000
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BEGIN:VEVENT
DTSTAMP:20241017T123610
DTSTART;TZID=America/Detroit:20241025T100000
DTEND;TZID=America/Detroit:20241025T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Gongjun Xu\, Associate Professor\, Department of Statistics\, University of Michigan
DESCRIPTION:Abstract: Generalized latent factor analysis not only provides a useful latent embedding approach in statistics and machine learning\, but also serves as a widely used tool across various scientific fields\, including psychometrics\, econometrics\, and social sciences. Ensuring the identifiability of latent factors and the loading matrix is essential for the model's estimability and interpretability\, and various identifiability conditions have been employed by practitioners. However\, fundamental statistical inference issues for latent factors and factor loadings under commonly used identifiability conditions remain largely unaddressed. In this work\, we focus on the maximum likelihood estimation for generalized factor models and establish statistical inference properties under popularly used identifiability conditions. The developed theory is further illustrated through numerical simulations and an application to a personality assessment dataset.\n\nhttps://sites.google.com/umich.edu/gongjunxu
UID:124541-21853171@events.umich.edu
URL:https://events.umich.edu/event/124541
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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