BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:iCalendar-Ruby
BEGIN:VEVENT
CATEGORIES:Academics,Lectures/Seminars
DESCRIPTION:Speaker: Prof. Min Wang\, MTU\;\n\nAbstract:\n\nIn this talk\,
we explore Bayesian approaches for the hypothesis testing problem in multiw
ay analysis-of-variance (ANOVA) models. We first specialize the result in a
two-sample scenario as an intermediate step toward developing the Bayes fa
ctors for ANOVA designs. Given that the design matrix is not necessarily of
full rank\, we adopt the sum-to-zero constraint for uniqueness and employ
the singular value decomposition (SVD) method to reparameterize the model t
o get rid of the additional constraint. We then derive the Bayes factors un
der a class of Zellnerâ€™s g-priors. In particular\, we examine asymptotic pr
operties of the proposed procedures with a diverging dimensionality. Our re
sults indicate that two commonly used hyper-priors on g (the Zellner-Siow p
rior and the beta-prime prior) yield inconsistent Bayes factors due to the
presence of an inconsistency region around the null model. We propose a new
class of hyper-priors to avoid this inconsistency problem. Simulation stud
ies on two-way ANOVA models are conducted to compare the performance of the
proposed priors with that of some existing ones in the literature.
DTEND:20170209T190500Z
DTSTAMP:20230128T204150Z
DTSTART:20170209T180500Z
GEO:47.118149;-88.546013
LOCATION:Fisher Hall\, 325
SEQUENCE:0
SUMMARY:Mixtures of g-priors for analysis of variance models with a divergi
ng number of parameters
UID:tag:localist.com\,2008:EventInstance_2614731
URL:https://events.mtu.edu/event/mixtures_of_g-priors_for_analysis_of_varia
nce_models_with_a_diverging_number_of_parameters
END:VEVENT
END:VCALENDAR