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DTSTART:20070311T020000
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DTSTAMP:20240903T115113
DTSTART;TZID=America/Detroit:20240913T100000
DTEND;TZID=America/Detroit:20240913T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Zongming Ma\, Professor\, Department of Statistics and Data Science\, Yale University
DESCRIPTION:Abstract: The need for multimodal data integration arises naturally when multiple complementary sets of features are measured on the same sample. Under a dependent multifactor model\, we develop a fully data-driven orchestrated approximate message passing algorithm for integrating information across these feature sets to achieve statistically optimal signal recovery. In practice\, these reference data sets are often queried later by new subjects that are only partially observed. Leveraging on asymptotic normality of estimates generated by our data integration method\, we further develop an asymptotically valid prediction set for the latent representation of any such query subject. We demonstrate the prowess of both the data integration and the prediction set construction algorithms on a tri-modal single-cell dataset.\n\nhttps://zmastat.github.io/
UID:124532-21853148@events.umich.edu
URL:https://events.umich.edu/event/124532
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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