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VERSION:2.0
X-WR-CALNAME:Department of Statistics Seminar
X-WR-TIMEZONE:Pacific/Auckland
PRODID:-//Department of Statistics//NONSGML Stephen Cope//EN
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TZID:Pacific/Auckland
BEGIN:DAYLIGHT
TZOFFSETFROM:+1200
TZOFFSETTO:+1300
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DTSTART:19700927T020000
RRULE:FREQ=YEARLY;BYMONTH=9;BYDAY=-1SU
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TZOFFSETFROM:+1300
TZOFFSETTO:+1200
TZNAME:NZST
DTSTART:19700405T030000
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BEGIN:VEVENT
DTSTART;TZID=Pacific/Auckland:20211208T140000
DURATION:PT60M
SUMMARY:Estimating the approximation error for the saddlepoint maximum li
kelihood estimate
UID:2021-12-08-1400@vcal.stat.auckland.ac.nz
DESCRIPTION:Saddlepoint approximation to a density function is increasing
ly being used primarily because of its immense accuracy. A common applic
ation of this approximation is to interpret it as a likelihood function
\, especially when the true likelihood function does not exist or is int
ractable. The application aims at obtaining parameter estimates using th
e likelihood function based on saddlepoint approximation. This study exa
mines the likelihood function (based on first and second-order saddlepoi
nt approximation) to estimate the difference between the true but unknow
n maximum likelihood estimation (MLE) estimates and the saddlepoint-base
d MLEs. We propose an expression to estimate this difference (error) by
computing the gradient of the neglected term in the first-order saddlepo
int approximation. Then using common distributions whose true likelihood
functions are known to perform confirmatory tests on the proposed error
expression\, we show that the results are consistent with the differenc
e between the true MLEs and saddlepoint MLEs. These tests indicate that
the proposed formula could complement simulation studies\, which has bee
n widely used to justify the accuracy of such saddlepoint MLEs.
LOCATION:https://auckland.zoom.us/j/99638945519?pwd=eUx2RHBzVjhWY0JCTnc2T
0JsMk1CUT09
CREATED:20211024T223728Z
DTSTAMP:20211108T052114Z
LAST-MODIFIED:20211108T052114Z
SEQUENCE:3
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Pacific/Auckland:20211214T140000
DURATION:PT60M
SUMMARY:Investigating linkage bias in the Integrated Data Infrastructure
UID:2021-12-14-1400@vcal.stat.auckland.ac.nz
DESCRIPTION:Linked administrative data can provide rich information on a
wide range of outcomes\, and its usage in on the rise both in New Zealan
d and internationally. The Integrated Data Infrastructure (IDI) is a dat
abase maintained by Statistics New Zealand (Stats NZ) and contains linke
d administrative data at individual level. In the absence of unique pers
onal identifier\, probabilistic record linkage is performed which unavoi
dably would evoke linkage errors. However\, the majority of IDI analysis
is completed without understanding\, measuring or correcting for potent
ial linkage bias. We aim to quantify linkage errors in the IDI and provi
de feasible approaches to adjust for linkage biases in IDI analysis. In
this talk\, I will briefly explain how linkage errors (false links and m
issed links) may occur in the IDI\, followed by approaches on false link
and missed link identification. Some key limitations will also be addre
ssed.
LOCATION:https://auckland.zoom.us/j/94353887562?pwd=YkxoN2JMdVFDY0RxVUtlN
UR0VGtmZz09
CREATED:20211201T001509Z
DTSTAMP:20211201T001622Z
LAST-MODIFIED:20211201T001622Z
SEQUENCE:2
END:VEVENT
END:VCALENDAR