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DTSTART:20221106T020000
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UID:calendar.25595.field_event_date_2.0@math.wustl.edu
CREATED:20220722T150635Z
DESCRIPTION:Abstract: In modern data analysis, high dimensional data with
more variables than subjects is increasingly common. Two such cases where
this phenomenon arises is during mediation analysis and in distributed opt
imization. In chapter 2 we start with an overview of high dimensional stat
istics and mediation analysis to motivate the considered problems. In chap
ter 3 we motivate and prove properties for a new marginal screening proced
ure for performing high dimensional mediation analysis. This screening pro
cedure is shown via simulation to perform better than benchmark approaches
and is applied to a DNA methylation study. In chapter 4 we motivate and c
onstruct a cryptosystem that accurately performs distributed penalized qua
ntile regression in the high-dimensional setting using a divide-and-conque
r approach while preserving the privacy of subject data.\n\nHost: Nan Lin
DTSTART;TZID=America/Chicago:20220729T090000
DTEND;TZID=America/Chicago:20220729T103000
LAST-MODIFIED:20220722T150635Z
SUMMARY:PhD Thesis Defense: 'Dealing with Dimensionality: Problems and Tech
niques in High-Dimensional Statistics'
URL;TYPE=URI:https://math.wustl.edu/events/phd-thesis-defense-dealing-dimen
sionality-problems-and-techniques-high-dimensional-statistics
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