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UID:20251022T133331EDT-63435fkX7j@132.216.98.100
DTSTAMP:20251022T173331Z
DESCRIPTION:Title: Renewable Estimation and Incremental Inference in Stream
 ing Data Analysis\n\nAbstract: New data collection and storage technologie
 s have given rise to a new field of streaming data analytics\, including r
 eal-time statistical methodology for online data analyses. Streaming data 
 refers to high-throughput recordings with large volumes of observations ga
 thered sequentially and perpetually over time. Such type of data includes 
 national disease registry\, mobile health\, and disease surveillance\, amo
 ng others. This talk primarily concerns the development of a fast real-tim
 e statistical estimation and inference method for regression analysis\, wi
 th a particular objective of addressing challenges in streaming data stora
 ge and computational efficiency. Termed as renewable estimation\, this met
 hod enjoys strong theoretical guarantees\, including both asymptotic unbia
 sedness and estimation efficiency\, and fast computational speed. The key 
 technical novelty pertains to the fact that the proposed method uses curre
 nt data and summary statistics of historical data. The proposed algorithm 
 will be demonstrated in generalized linear models (GLM) for cross-sectiona
 l data. I will discuss both conceptual understanding and theoretical guara
 ntees of the method and illustrate its performance via numerical examples.
  This is joint work with my supervisor Professor Peter Song.\n\nhttps://ww
 w.luolsph.com/\n\nhttps://sph.umich.edu/biostat/phd-student-profiles/luo-l
 an.html\n\n \n
DTSTART:20200110T203000Z
DTEND:20200110T213000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Lan Luo (University of Michigan)
URL:/mathstat/channels/event/lan-luo-university-michig
 an-303118
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