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DTSTART:20190310T070000
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DTSTART;TZID=America/Toronto:20200107T220000
SEQUENCE:0
TRANSP:TRANSPARENT
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/department
 -seminar-lan-luo-university-michigan
LOCATION:M3 - Mathematics 3 200 University Avenue West Room: 3127 Waterloo 
 ON N2L 3G1 Canada
SUMMARY:Department seminar by Lan Luo\, University of Michigan
CLASS:PUBLIC
DESCRIPTION:RENEWABLE ESTIMATION AND INCREMENTAL INFERENCE IN STREAMING DAT
 A\nANALYSIS\n\n-------------------------\n\nNew data collection and storag
 e technologies have given rise to a new\nfield of streaming data analytics
 \, including real-time statistical\nmethodology for online data analyses. 
 Streaming data refers to\nhigh-throughput recordings with large volumes of
  observations gathered\nsequentially and perpetually over time. Such type 
 of data includes\nnational disease registry\, mobile health\, and disease 
 surveillance\,\namong others. This talk primarily concerns the development
  of a fast\nreal-time statistical estimation and inference method for regr
 ession\nanalysis\, with a particular objective of addressing challenges in
 \nstreaming data storage and computational efficiency. Termed as\nrenewabl
 e estimation\, this method enjoys strong theoretical\nguarantees\, includi
 ng both asymptotic unbiasedness and estimation\nefficiency\, and fast comp
 utational speed. The key technical novelty\npertains to the fact that the 
 proposed method uses current data and\nsummary statistics of historical da
 ta. The proposed algorithm will be\ndemonstrated in generalized linear mod
 els (GLM) for cross-sectional\ndata. I will discuss both conceptual unders
 tanding and theoretical\nguarantees of the method and illustrate its perfo
 rmance via numerical\nexamples. This is joint work with my supervisor Prof
 essor Peter Song.
DTSTAMP:20260411T011543Z
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