Statistical Inference, Learning and Models in Big Data

Beate Franke; Jean François Plante; Ribana Roscher; En shiun Annie Lee; Cathal Smyth; Armin Hatefi; Fuqi Chen; Einat Gil; Alexander Schwing; Alessandro Selvitella; Michael M. Hoffman; Roger Grosse; Dieter Hendricks; Nancy Reid

doi:10.1111/insr.12176

Statistical Inference, Learning and Models in Big Data

Beate Franke, Jean François Plante, Ribana Roscher, En shiun Annie Lee, Cathal Smyth, Armin Hatefi, Fuqi Chen, Einat Gil, Alexander Schwing, Alessandro Selvitella, Michael M. Hoffman, Roger Grosse, Dieter Hendricks, Nancy Reid

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.

Original language	English
Pages (from-to)	371-389
Number of pages	19
Journal	International Statistical Review
Volume	84
Issue number	3
DOIs	https://doi.org/10.1111/insr.12176
State	Published - 1 Dec 2016
Externally published	Yes

Keywords

aggregation
computational complexity
dimension reduction
high-dimensional data
networks
streaming data

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10.1111/insr.12176

Cite this

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AU - Hatefi, Armin

AU - Chen, Fuqi

AU - Gil, Einat

AU - Schwing, Alexander

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AU - Hendricks, Dieter

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Statistical Inference, Learning and Models in Big Data

Abstract

Keywords

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