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UID:ude20221012161500
CLASS:PUBLIC
SUMMARY:On the Strong Convergence of a Bayesian Parameter Estimator
DTSTART;TZID=Europe/Berlin:20221012T161500
DTEND;TZID=Europe/Berlin:20221012T174500
DTSTAMP:20221012T161500Z
LOCATION;ENCODING=QUOTED-PRINTABLE:Campus Campus Essen : S06 S00 A40
CONTACT:Frau Dr. Helene Kruse (Interdisziplinäres Zentrum für Bildungsforschung)
DESCRIPTION:Frau Dr. Helene Kruse (Interdisziplinäres Zentrum für Bildungsforschung)
On the Strong Convergence of a Bayesian Parameter Estimator
Prof. Dr. Tenko Raykov, Michigan State University 
This talk is concerned with the large-sample behavior of a popular Bayesian estimator.  The property of strong convergence of the posterior median estimator is discussed, which states numerical convergence with probability 1 of the resulting estimates to the population parameter value as sample size increases without bound.  This property is stronger than the consistency and convergence in distribution of that estimator, which are implied from it and have been commonly referred to in the literature.  A couple of numerical examples are utilized to illustrate this almost sure convergence of a Bayesian estimator of a regression slope in a setting with errors in predictors and of a latent correlation in a multiple indicator setting, which are of particular interest in social, behavioral, medical, marketing, and economics research.  The paper contributes to the body of research on optimal statistical features of Bayesian estimates, and conc
Wednesday, 12. October 2022
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