Title	Online Updating of Statistical Inference in the Big Data Setting
Creator	Schifano, Elizabeth
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2014-02-12
Description	We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. The online updating framework in the linear model setting introduces predictive residuals that can be used to test the goodness-of-fit of the hypothesized model. In simulation studies, our approach compares favorably with competing approaches in terms of timing and accuracy. Joint work with Ming-Hui Chen, Jun Yan, Chun Wang, Jing Wu (Department of Statistics, University of Connecticut)
Extent	41 minutes
Subject	Mathematics; Statistics; Biology and other natural sciences; Applied statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Connecticut
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2014-08-07
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI	10.14288/1.0043891
URI	http://hdl.handle.net/2429/49843
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
Aggregated Source Repository	DSpace

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Online Updating of Statistical Inference in the Big Data Setting Schifano, Elizabeth

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