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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20230320T110445Z
DTSTART:20230426T130000Z
DTEND:20230426T140000Z
SUMMARY:Antony Overstall - Approximate Bayesian Multiple systems estimati
on with partial classification (- in person)
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}a7z-l7t7suz
2-i8gghf
DESCRIPTION:Antony Overstall\, Associate Professor in Statistics in the D
epartment of Mathematical Sciences at the University of Southampton is o
ur speaker for the Statistics seminar series.\n\nTitle: Approximate Baye
sian Multiple systems estimation with partial classification\n\nAbstract
: Multiple systems estimation (MSE) refers to statistical methodology u
sed to estimate unknown and elusive human population sizes\, from admini
strative data. These population sizes usually consist of vulnerable peop
le\, e.g. individuals subjected to human trafficking or who are injectin
g drug users. Typically\, in MSE\, individuals from the target populatio
n are observed on a series of administrative lists (e.g. police records\
, hospital records\, etc). Individuals can appear on more than one list
and so these lists are matched so that the count of individuals observed
on each combination of lists is determined. These data can then be used
to estimate the unknown total population size and quantify uncertainty
in this estimation. In some cases\, it is not possible to match between
certain lists\, i.e. partial classifiication. Subsequently\, only linear
combinations of certain counts are observed\, which can render the like
lihood computationally expensive to evaluate. Borrowing a saddlepoint li
kelihood approximation from the frequentist MSE literature\, this talk d
evelops a Bayesian MSE approach for partial classification which can acc
ount for model uncertainty and can be extended to other missing data MSE
problems.\n\nVenue:\nFrank Adams Seminar Room 2 \nAlan Turing Building
\nManchester\nM13 9PL
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Frank Adams Seminar Room 2 \, Alan Turing Building \, Upper Broo
k street\, Manchester\, M13 9PL
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