Just Published: Computational Statistics in Data Science

Wiley has published a new compendium, Computational Statistics in Data Science, from co-editors Walter W. Piegorsch, Richard A. Levine, Hao Helen Zhang, and Thomas C. M. Lee.  The book delivers a collection of concepts, theories, techniques, and practices in computational statistics, with multiple sections devoted to the broad areas of simulation-based approaches, statistical learning, quantitative visualization, high-performance computing, high-dimensional data analysis, numerical approximations, and optimization. 

Key, specific chapters offer modern and accessible presentations of up-to-date techniques from a variety of leading experts in the field.  The presentations delve into how computational and statistical technologies are applied in contemporary data science and also act to build springboards that can develop further research advances. 


About the Authors:

is Professor of Mathematics at the University of Arizona. He is the Director of Statistical Research & Education at the University’s BIO5 Institute.  He is also a Professor of Mathematics, a Professor of Public Health, and a Member and former Chair of the University’s Graduate Interdisciplinary Program (GIDP) in Statistics.  Dr Piegorsch studies data science for environmental problems, with emphasis on informatics for environmental hazards and risk assessment. He has written many books for Wiley and is one of the Co-Editors of Wiley’s Encyclopedia of Environmetrics and Wiley StatsRef.

RICHARD A. LEVINE is Professor of Statistics at San Diego State University and Faculty Advisor overseeing the Statistical Modeling Group in SDSU Analytic Studies and Institutional Research. He is former Chair of the SDSU Department of Mathematics and Statistics and past Editor of the Journal of Computational and Graphical Statistics. He is Associate Editor for Statistics of the Notices of the American Mathematical Society and is a fellow of the American Statistical Association.

HAO HELEN ZHANG is Professor of Mathematics Statistics at the University of Arizona and Chair of the UA Graduate Interdisciplinary Program (GIDP) in Statistics and Data Science. Her research areas include statistical machine learning, nonparametric smoothing, and high-dimensional data analysis. She is Editor-in-Chief of the Wiley journal STAT and Associate Editor for JASA and JRSSB. She is a Fellow of the American Statistical Association (ASA), Fellow of the Institute of Mathematical Statistics (IMS), and 2019 IMS Medallion Lecturer.

THOMAS C. M. LEE is Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis. He is a former Chair of the Department of Statistics at the same institution and a past editor of the Journal of Computational and Graphical Statistics. He is an elected fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics.





More Details