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DTSTART:20171105T020000
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DTSTART:20180311T020000
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UID:calendar.94381.field_date.0@www.wpi.edu
DTSTAMP:20191213T020311Z
CREATED:20180110T143106Z
DESCRIPTION:Description of Event: \n\n\n\nStatistic Seminar – Monday\, Febr
uary 12\, 2018\n\n\n\nMin-ge Xie\n\n\n\nRutgers University\n\n\n\nDepartme
nt of Statistics and Biostatistics\n\n\n\nTitle: Confidence Distribution (
CD) and Approximate Computing for Frequentist Inference\n\n\n\nAbstract:
A confidence distribution (CD) is a sample-dependent distribution function
that can serve as a distribution estimate\, contrasting with a point or i
nterval estimate\, of an unknown parameter. It can represent confidence in
tervals (regions) of all levels for the parameter. It is to provide “simpl
e and interpretable summaries of what can reasonably be learned from data\
,” as well as meaningful answers for all questions in statistical inferenc
e. An emerging theme is “Any statistical approach\, regardless of being fr
equentist\, fiducial or Bayesian\, can potentially be unified under the co
ncept of confidence distributions\, as long as it can be used to derive co
nfidence intervals of all levels\, exactly or asymptotically.”\n\n\n\nIn t
his talk\, we articulate the logic behind the CD developments and also\, t
o illustrate its utility for methodology development\, present a likelihoo
d-free approximate computing method\, called Approximate CD computing (ACC
). ACC is a frequentist analog of Approximate Bayesian computing (ABC)\,
a Bayesian approximate computing method that has grown increasingly popula
r since early applications in population genetics. However\, complications
arise in the theoretical justification for Bayesian inference when using
ABC with a non-sufficient summary statistic. We seek to re-frame ABC withi
n a frequentist context and justify its performance by the frequency cover
age rate. In doing so\, we develop the ACC method and provide theoretical
support for the use of non-sufficient summary statistics in likelihood-fre
e methods. Furthermore\, we demonstrate that ACC extends the scope of ABC
to include data-dependent priors without damaging the inferential integrit
y but to increase computing efficiency. We will supplement the theory with
both simulation and real data analysis to illustrate the benefits of the
ACC method\, namely the potential for broader applications than ABC and th
e increased computing speed compared to ABC.
DTSTART;TZID=America/New_York:20180212T110000
DTEND;TZID=America/New_York:20180212T120000
LAST-MODIFIED:20180123T154006Z
LOCATION:Stratton Hall
SUMMARY:Mathematical Sciences - Statistics Seminar Minge Xie (Rutgers Unive
rsity) Stratton Hall 304 'Confidence Distribution (CD) and Approximate Com
puting for Frequentist Inference' by
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/mathematical-sciences
-statistics-seminar-minge-xie-rutgers-university-stratton
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