Title	Statistical Aggregation in Massive Data Environment
Creator	Lin, Nan
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2014-02-12
Description	Due to their size and complexity, massive data sets bring many computational challenges for statistical analysis, such as overcoming the memory limitation and improving computational efficiency of traditional statistical methods. In this talk, I will discuss the statistical aggregation strategy to conquer such challenges posed by massive data sets. Statistical aggregation partitions the entire data set into smaller subsets, compresses each subset into certain low-dimensional summary statistics and aggregates the summary statistics to approximate the desired computation based on the entire data. Results from statistical aggregation are required to be asymptotically equivalent. Statistical aggregation is particularly useful to support sophisticated statistical analyses for online analytical processing in data cubes. We will detail its application to two large families of statistical methods, estimating equation estimation and U-statistics.
Extent	40 minutes
Subject	Mathematics; Statistics; Biology and other natural sciences; Applied statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Washington University in St. louis
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	10.14288/1.0043890
URI	http://hdl.handle.net/2429/49844
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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Statistical Aggregation in Massive Data Environment Lin, Nan

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