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Effective Statistical Methods for Big Data Analytics
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Author(s): Cheng Meng (University of Georgia, USA), Ye Wang (University of Georgia, USA), Xinlian Zhang (University of Georgia, USA), Abhyuday Mandal (University of Georgia, USA), Wenxuan Zhong (University of Georgia, USA)and Ping Ma (University of Georgia, USA)
Copyright: 2017
Pages: 20
Source title:
Handbook of Research on Applied Cybernetics and Systems Science
Source Author(s)/Editor(s): Snehanshu Saha (PESIT South Campus, India), Abhyuday Mandal (University of Georgia, USA), Anand Narasimhamurthy (BITS Hyderabad, India), Sarasvathi V (PESIT- Bangalore South Campus, India)and Shivappa Sangam (UGC, India)
DOI: 10.4018/978-1-5225-2498-4.ch014
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Abstract
With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.
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