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ORGANIZER;CN=Henry Reeve:mailto:[log in to unmask]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=STATS-SEM@
JISCMAIL.AC.UK:mailto:[log in to unmask]
DESCRIPTION;LANGUAGE=en-GB:Hi Everyone\,\n\nOn Friday the 30th of September
we will have a Statistics seminar at 2pm (room TBA). Our speaker will be
Rajen Shah from the University of Cambridge. Please see below for the abst
ract.\n\nBefore the talk we will have a lunch in the common room at 1pm. P
lease complete the form (here) by 4
pm on Monday the 19th of September here if you’d like to attend the lunc
h.\n\nPlease also let me know if you have speakers you’d like to invite.
\n\nAll the best\,\nHenry\n\n\n\nSpeaker: Rajen Shah\nTime: 2pm on Friday
the 30th of September\nTitle: The Projected Covariance Measure for assumpt
ion-lean variable significance testing\nAbstract: Testing the significance
of a variable or group of variables X for predicting a response Y given a
dditional covariates Z\, is a ubiquitous task in statistics. A simple but
common approach is to specify a linear model\, and then test whether the r
egression coefficient for X is non-zero. However\, when the model is missp
ecified\, as will invariably be the case\, the test may have poor power\,
for example when X is involved in complex interactions\, or lead to many f
alse rejections.\nIn this work we study the problem of testing the model-f
ree null of conditional mean independence\, i.e. that the conditional mean
of Y given X and Z does not depend on X. We propose a simple and general
framework that can leverage flexible nonparametric or machine learning met
hods\, such as additive models or boosted trees\, to yield both robust err
or control and high power. The procedure involves using these methods to p
erform regressions\, first to estimate a form of projection of Y on X and
Z using one half of the data\, and then to estimate the expected condition
al covariance between this projection and Y on the remaining half of the d
ata. While the approach is general\, we show that a version of our procedu
re using spline regression achieves what we show is the minimax optimal ra
te in this nonparametric testing problem.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\
n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\
n\n\n\n\n\n\n\n\n\nPayables Team Mailbox [log in to unmask][log in to unmask]>\n\nEmail – seminar\n\n\nLunch\n\nNEED > a
week notice!!!\n\nMicrosoft Form – send to all Stats…\nPa-Maths@brist
ol.ac.uk\nTime\nPlace\n\nReimbursement form
for non-staff members University of Bristol\n\nBudget code..\n\n\n\n\n\n3
emails in a batch…\n\n\nIn TB1 online…\n\nPrompt at the beginning…\
n\nFriday afternoon slots:\n3pm – 5pm\n\nPreviously offline:\n2pm-4pm\n\
n45 minutes long talk + 5mins Q&A\n\n\nStarts Oct 1st\n\nQuestions for Son
g:\n\n\n1. Fortnightly\n\n\n2. How to edit web-page\n\n\n\n3.
Email speakers – how far in advance\n\nA month and a half in advance\n\n
Earlier for more senior people\n\nTwo consecutive dates\n\n\n * How
long typically wait for a reply before prompting?\n\nAlways respond within
a week…\n\n\n4. Choice of areas?\n\n\n\n5. Tea + Coffee for ret
urn to office\n\n\n6. Travel costs\n\n\n\n7. Speaker out to dinner
/drink..?\n\n\n\n8. Scheduling tool?\n\n
UID:040000008200E00074C5B7101A82E00800000000B2264B631BC8D801000000000000000
010000000E22D11B2FE0C3A469DBA9A3DF9EC0EB7
SUMMARY;LANGUAGE=en-GB:Statistics seminar: Rajen Shah
DTSTART;TZID=GMT Standard Time:20220930T130000
DTEND;TZID=GMT Standard Time:20220930T150000
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20220914T092137Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
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