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Refining the concept of scientific inference when working with big data : proceedings of a workshop
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | Refining the concept of scientific inference when working with big data : proceedings of a workshop/ Ben A. Wender, rapporteur ; Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and their Applications, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine. |
| 개인저자 | Wender, Ben A.,rapporteur. |
| 단체저자명 | National Academies of Sciences, Engineering, and Medicine (U.S.). Committee on Applied and Theoretical Statistics,issuing body. |
| 발행사항 | Washington, DC: the National Academies Press, [2017]. |
| 형태사항 | 1 online resource (xii, 102 p.): color illustrations. |
| ISBN | 9780309454452 030945445X |
| 서지주기 | Includes bibliographical references (pages 69-73). |
| 내용주기 | Introduction -- Framing the workshop -- Inference about discoveries basedon integration of diverse data sets -- Inference about causal discoveries driven by large observational data -- Inference when regularization is used to simplify fitting of high-dimensional models -- Panel discussion -- References -- Appendixes. |
| 요약 | "The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop"--Publisher's description. |
| 회의명 | Refining the Concept of Scientific Inference When Working with Big Data (Workshop) (2016 : Washington, D.C.) |
| 일반주제명 | Big data --Congresses. Mathematical statistics --Congresses. Science --Methodology --Congresses. Experimental design --Congresses. Big data. Experimental design. Mathematical statistics. Science --Methodology. MATHEMATICS / Essays MATHEMATICS / Pre-Calculus MATHEMATICS / Reference |
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