G. Schissler
The University of Nevada
Reno, NV
USA
Rodney Sparapani
Institute for Health and Equity
Medical College of Wisconsin
Milwaukee, WI
USA
Kelly M. Spoon
Computational Science Research Center
San Diego State University
San Diego, CA
USA
Xiaogang Su
Department of Mathematical Sciences
University of Texas
El Paso, TX
USA
Marc A. Suchard
University of California
Los Angeles, CA
USA
Ying Sun
King Abdullah University of Science and Technology
Thuwal
Saudi Arabia
Nola du Toit
NORC at the University of Chicago
Chicago, IL
USA
Dootika Vats
Indian Institute of Technology Kanpur
Kanpur
India
Matti Vihola
University of Jyväskylä
Jyväskylä
Finland
Justin Wang
University of California at Davis
Davis, CA
USA
Will Wei Sun
Purdue University
West Lafayette, IN
USA
Leland Wilkinson
H2O.ai, Mountain View
California
USA
and
University of Illinois at Chicago
Chicago, IL
USA
Joong‐Ho Won
Seoul National University
Seoul
South Korea
Yichao Wu
University of Illinois at Chicago
Chicago, IL
USA
Min‐ge Xie
Rutgers University
Piscataway, NJ
USA
Ming Yan
Michigan State University
East Lansing, MI
USA
Yuling Yao
Columbia University
New York, NY
USA
and
Center for Computational Mathematics
Flatiron Institute
New York, NY
USA
Chun Yip Yau
Chinese University of Hong Kong
Shatin
Hong Kong
Hao H. Zhang
University of Arizona
Tucson, AZ
USA
Hua Zhou
University of California
Los Angeles, CA
USA
Preface
Computational statistics is a core area of modern statistical science and its connections to data science represent an ever‐growing area of study. One of its important features is that the underlying technology changes quite rapidly, riding on the back of advances in computer hardware and statistical software. In this compendium we present a series of expositions that explore the intermediate and advanced concepts, theories, techniques, and practices that act to expand this rapidly evolving field. We hope that scholars and investigators will use the presentations to inform themselves on how modern computational and statistical technologies are applied, and also to build springboards that can develop their further research. Readers will require knowledge of fundamental statistical methods and, depending on the topic of interest they peruse, any advanced statistical aspects necessary to understand and conduct the technical computing procedures.
The presentation begins with a thoughtful introduction on how we should view Computational Statistics & Data Science in the 21st Century (Holbrook, et al.), followed by a careful tour of contemporary Statistical Software (Schissler, et al.). Topics that follow address a variety of issues, collected into broad topic areas such as Simulation‐based Methods, Statistical Learning, Quantitative Visualization,
