Design and Modeling for Computer Experiments

Artikelnummer: 978-0-367-57800-8
Einband: Kartonierter Einband (Kt)
Verfügbarkeit: Lieferbar in ca. 10-20 Arbeitstagen
CHF 90.00
decrease increase
This book blends a modern statistical approach with extensive engineering applications and clearly delineates the steps for successfully modeling a problem and analyzing it to find the solution. It introduces basic concepts, then fully examines computer experiment design. The authors present the popular space-filling designs - like Latin hypercube". . . very well-organized text . . . makes a very valuable contribution to the field. I highly recommend it for anyone trying to learn design and modeling techniques for computer experiments. In particular, it will be a useful professional reference for scientists and engineers in practicing computer experiments, a comprehensive resource book for statisticians interested in developing new techniques for designing and modeling computation experiments, and an excellent book for undergraduate and graduate students. The authors' careful and thorough presentation style makes the book a very enjoyable read." - Hao Helen Zhang, North Carolina State University, in JASA, December 2008
This book blends a modern statistical approach with extensive engineering applications and clearly delineates the steps for successfully modeling a problem and analyzing it to find the solution. It introduces basic concepts, then fully examines computer experiment design. The authors present the popular space-filling designs - like Latin hypercube". . . very well-organized text . . . makes a very valuable contribution to the field. I highly recommend it for anyone trying to learn design and modeling techniques for computer experiments. In particular, it will be a useful professional reference for scientists and engineers in practicing computer experiments, a comprehensive resource book for statisticians interested in developing new techniques for designing and modeling computation experiments, and an excellent book for undergraduate and graduate students. The authors' careful and thorough presentation style makes the book a very enjoyable read." - Hao Helen Zhang, North Carolina State University, in JASA, December 2008
Schreiben Sie Ihre eigene Bewertung
  • Nur registrierte Benutzer können Produkte bewerten
*
*
Schlecht
Sehr gut
*
*
*
*
VerlagTaylor and Francis
EinbandKartonierter Einband (Kt)
Erscheinungsjahr2020
Seitenangabe302 S.
AusgabekennzeichenEnglisch
MasseH23.4 cm x B15.6 cm 560 g
CoverlagChapman & Hall/CRC (Imprint/Brand)
ReiheChapman & Hall/CRC Computer Science & Data Analysis
AutorFang Kai-Tai / Li Runze / Sudjianto Agus

Alle Bände der Reihe "Chapman & Hall/CRC Computer Science & Data Analysis"

Über den Autor Fang Kai-Tai

Kai-Tai Fang is a reputable statistician. He was educated at Peking University and The Chinese Academy of Sciences for undergraduate and postgraduate studies. He was elected as a Fellow by the Institute of Mathematical Statistics (IMS) in 1992 and a Fellow by the American Statistical Association (ASA) in 2001 as well as an elective member of the International Statistical Institute (ISI) in 1985. Professor Fang visited Yale University and Stanford University for two years and was invited as a Guest Professor at the Swiss Federal Institute of Technology and a Visiting Professor at the University of North Carolina at Chapel Hill. He was Chair Professor of the Department of Mathematics at Hong Kong Baptist University from 1993 to January 2006. Now, he is a Chair Professor at BNUHKBU United International College. His research interests are in statistics and mathematics, specifically experimental design, multivariate analysis, and data mining. He published 24 books (including six monographs in English) and more than 300 referred papers.Huajun Ye received a Bachelor and a Master's degrees in Probability and Mathematical Statistics from Peking University in 1999 and 2002, respectively. He received a PhD in Statistics from Manchester University, U.K. 2005, and his PhD research on covariance structures modeling of longitudinal data. In 2007, He joined BNU-HKBU United International College as an Assistant Professor. Now, he is a full Professor in the Department of Statistics and Data Science at BNU-HKBU United International College. His research interests include statistical modeling, inference, financial risk management, and statistical representative points. More than ten research papers have been published in international journals and conferences, including Biometrika, Mathematics, Journal of Complexity, Journal of Statistical Computation and Simulation, etc.Yongdao Zhou received a B.S. degree in pure mathematics in 2002 and M.S. and Ph.D. in Statistics in 2005 and 2008, respectively, from Sichuan University, China. He was a postdoctoral fellow at HKBU-UIC Joint Institute of Research Studies. Then, he joined Sichuan University and was a full professor after 2015. In 2017, he joined Nankai University, where he is presently a full professor in statistics. He visited UCLA, the University of Manchester, the National University of Singapore, and Simon Fraser University as a visiting scholar. His research agenda focuses on experimental design and big data analysis. He published over 70 papers, such as in JRSSB, JASA, Biometrika, and IEEE TKDE, as well as eight monographs and textbooks. His research publications have won two best paper awards.

Weitere Titel von Fang Kai-Tai

Produktbewertungen
Nur registrierte Benutzer können Produkte bewerten