Computational Statistics in Data Science

Library of Congress Cataloging‐in‐Publication Data

ISBN 9781119561071 (hardback)

Cover Design: Wiley

Cover Image: © goja1/Shutterstock

Ayodele Adebiyi

Landmark University

Omu‐Aran, Kwara

Nigeria

Anirban Bhattacharya

Texas A&M University

College Station, TX

USA

Peter Calhoun

Jaeb Center for Health Research

Tampa, FL

USA

Wu Changye

Université Paris Dauphine PSL

Paris

France

Xueying Chen

Novartis Pharmaceuticals Corp.

East Hanover, NJ

USA

Jerry Q. Cheng

New York Institute of Technology

New York, NY

USA

Hugh Chipman

Acadia University

Wolfville, Nova Scotia

Canada

Olawande Daramola

Cape Peninsula University of Technology

Cape Town

South Africa

Katarina Domijan

Maynooth University

Maynooth

Ireland

Víctor Elvira

School of Mathematics

University of Edinburgh, Edinburgh

UK

Juanjuan Fan

Department of Mathematics and Statistics

San Diego State University

San Diego, CA

USA

James M. Flegal

University of California

Riverside, CA

USA

Marc G. Genton

King Abdullah University of Science and Technology

Thuwal

Saudi Arabia

Edward George

The Wharton School

University of Pennsylvania

Philadelphia, PA

USA

Robert B. Gramacy

Virginia Polytechnic Institute and State University

Blacksburg, VA

USA

Richard Hahn

The School of Mathematical and Statistical Sciences

Arizona State University

Tempe, AZ

USA

Botao Hao

DeepMind

London

UK

Andrew J. Holbrook

University of California

Los Angeles, CA

USA

Mingyi Hong

University of Minnesota

Minneapolis, MN

USA

Cho‐Jui Hsieh

University of California

Los Angeles, CA

USA

Jessica Hullman

Northwestern University

Evanston, IL

USA

David R. Hunter

Penn State University

State College, PA

USA

Catherine B. Hurley

Maynooth University

Maynooth

Ireland

Xiang Ji

Tulane University

New Orleans, LA

USA

Adam M. Johansen

University of Warwick

Coventry

UK

James E. Johndrow

University of Pennsylvania

Philadelphia, PA

USA

Galin L. Jones

University of Minnesota

Twin‐Cities Minneapolis, MN

USA

Seung Jun Shin

Korea University

Seoul

South Korea

Matthew Kay

Northwestern University

Evanston, IL

USA

Alexander D. Knudson

The University of Nevada

Reno, NV

USA

Taiwo Kolajo

Federal University Lokoja

Lokoja

Nigeria

and

Covenant University

Ota

Nigeria

Alfonso Landeros

University of California

Los Angeles, CA

USA

Kenneth Lange

University of California

Los Angeles, CA

USA

Thomas C.M. Lee

University of California at Davis

Davis, CA

USA

Richard A. Levine

Department of Mathematics and Statistics

San Diego State University

San Diego, CA

USA

Hongzhe Li

University of Pennsylvania

Philadelphia, PA

USA

Jia Li

The Pennsylvania State University

University Park, PA

USA

Lexin Li

University of California

Berkeley, CA

USA

Yao Li

University of North Carolina at Chapel Hill

Chapel Hill, NC

USA

Yufeng Liu

University of North Carolina at Chapel Hill

Chapel Hill, NC

USA

Rong Ma

University of Pennsylvania

Philadelphia, PA

USA

Shiqian Ma

University of California

Davis, CA

USA

Luca Martino

Universidad Rey Juan Carlos de Madrid

Madrid

Spain

Robert McCulloch

The School of Mathematical and Statistical Sciences

Arizona State University

Tempe, AZ

USA

Weibin Mo

University of North Carolina at Chapel Hill

Chapel Hill, NC

USA

Edward Mulrow

NORC at the University of Chicago

Chicago, IL

USA

Akihiko Nishimura

Johns Hopkins University

Baltimore, MD

USA

Lace Padilla

University of California

Merced, CA

USA

Vincent A. Pisztora

The Pennsylvania State University

University Park, PA

USA

Matthew Pratola

The Ohio State University

Columbus, OH

USA

Christian P. Robert

Université Paris Dauphine PSL

Paris

France

and

University of Warwick

Coventry

UK

Alfred 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

H 2O.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

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.

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