Editors:
- Presents current statistical challenges in production engineering with big data
- Combines research from both statistics and production engineering
- Presents a wide range of statistical methods and applications for big data in production engineering
Part of the book series: ICSA Book Series in Statistics (ICSABSS)
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Table of contents (12 papers)
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Front Matter
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Design and Collection of Big Data
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Front Matter
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Operation/Production Decision Making
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Front Matter
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Reliability and Health Management
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Front Matter
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Recent Advances in Statistical Methods
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Front Matter
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About this book
Editors and Affiliations
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Oregon Health & Science University , Portland, USA
Dongseok Choi
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Pukyong National University , Busan, Korea (Republic of)
Daeheung Jang
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Stanford University, Stanford, USA
Tze Leung Lai
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Seoul National University, Gwanak-gu, Seoul, Korea (Republic of)
Youngjo Lee
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Stanford University , Stanford, USA
Ying Lu
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University of Michigan–Ann Arbor, Ann Arbor, USA
Jun Ni
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University of Wisconsin–Madison, Madison, USA
Peter Qian
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University of Florida, Gainesville, USA
Peihua Qiu
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University of Chicago , Chicago, USA
George Tiao
Bibliographic Information
Book Title: Proceedings of the Pacific Rim Statistical Conference for Production Engineering
Book Subtitle: Big Data, Production Engineering and Statistics
Editors: Dongseok Choi, Daeheung Jang, Tze Leung Lai, Youngjo Lee, Ying Lu, Jun Ni, Peter Qian, Peihua Qiu, … George Tiao
Series Title: ICSA Book Series in Statistics
DOI: https://doi.org/10.1007/978-981-10-8168-2
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2018
Hardcover ISBN: 978-981-10-8167-5Published: 28 March 2018
Softcover ISBN: 978-981-13-4084-0Published: 22 December 2018
eBook ISBN: 978-981-10-8168-2Published: 27 March 2018
Series ISSN: 2199-0980
Series E-ISSN: 2199-0999
Edition Number: 1
Number of Pages: VIII, 170
Number of Illustrations: 7 b/w illustrations, 29 illustrations in colour
Topics: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Big Data/Analytics