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- Title
Statistical Inference, Learning and Models in Big Data.
- Authors
Franke, Beate; Plante, Jean‐François; Roscher, Ribana; Lee, En‐shiun Annie; Smyth, Cathal; Hatefi, Armin; Chen, Fuqi; Gil, Einat; Schwing, Alexander; Selvitella, Alessandro; Hoffman, Michael M.; Grosse, Roger; Hendricks, Dieter; Reid, Nancy
- 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.
- Subjects
BIG data; INFERENTIAL statistics; COMPUTATIONAL complexity; DIMENSION reduction (Statistics); STATISTICAL models; MACHINE learning; ELECTRONIC data processing
- Publication
International Statistical Review, 2016, Vol 84, Issue 3, p371
- ISSN
0306-7734
- Publication type
Article
- DOI
10.1111/insr.12176