BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.2//
METHOD:PUBLISH
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:STANDARD
DTSTART:20211107T020000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20220313T020000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:calendar.2056.events_uoft_date.0@www.statistics.utoronto.ca
CREATED:20220118T194754Z
DESCRIPTION:\nWhen and Where: \nMonday, January 31, 2022 3:30 pm to 4:30
pm \n Online \n\nSpeakers \nBenjamin Bolker \n\nDescription: \nStatistical
methods can target exploration, prediction, or inference. While big-dat
a applications have emphasized prediction, inference remains important;
in particular, inference is closely related to assessing the uncertainty
of coefficients and predictions. Data-driven methods for model selection a
nd tuning minimize prediction error by trading bias for variance, but the
y are rarely (never?) able to narrow confidence intervals or increase cert
ainty. If used naively, popular methods of data-driven model selection an
d tuning lead to overconfidence. Post-selection inference, a non-naive me
thod of accounting for the effects of data-driven model tuning, rely on s
trong assumptions. Researchers should should recognize how hard it is to q
uantify uncertainty reliably when they use data-driven model tuning, and
in many cases should abstain from tuning altogether.Please join the event.
About Benjamin BolkerDr. Benjamin Bolker completed an undergraduate degree
in mathematics and physics at Yale University and a Ph.D. in Zoology at C
ambridge University, working on the dynamics of measles epidemics. He did
a postdoc at Princeton University in ecology and evolutionary biology on
spatial dynamics of plant and host-parasite communities, beginning a facu
lty position at the Department of Zoology (later Biology) at the Universit
y of Florida in 1999. He moved to McMaster University in 2010, where he h
as a joint appointment in Mathematics & Statistics and Biology and directs
the School of Computational Science and Engineering. His research ranges
broadly across ecology, evolution, and epidemiology, applying mathemati
cal, statistical, and computational tools. He is especially interested i
n problems that involve parasites and disease, spatial population dynamic
s, estimation and inference of model parameters from observational data,
or all three. In addition to many research papers, he is the author of t
wo books (Ecological Models and Data in R and A Very Short Introduction to
Infectious Disease, with Marta Wayne) and the author or maintainer of se
veral widely used R packages. \n\nContact Information: \n CANSSI Ontario
\n\nCategories \n Data Science ARES \n\nAudiences \n FacultyGraduate Stude
nts
DTSTART;TZID=America/New_York:20220131T153000
DTEND;TZID=America/New_York:20220131T163000
LAST-MODIFIED:20220201T201134Z
SUMMARY:No Free Lunch in Inference
URL;TYPE=URI:https://www.statistics.utoronto.ca/events/no-free-lunch-infere
nce
END:VEVENT
END:VCALENDAR