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 Srlvitella, Micahel M. Hoffman, Roger Grosse, Dieter Hendricks, Nancy Reid

Research output: Contribution to journalArticlepeer-review

41 Citations (SciVal)


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 languageEnglish
Article number84
Pages (from-to)371-389
JournalInternational Statistical Review
Issue number3
Publication statusPublished - 1 Dec 2016


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


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