Artificial Intelligence, Big Data and Data Science in Statistics: Challenges and Solutions in Environmetrics, the Natural Sciences and Technology - Hardcover

Über die Autorin bzw. den Autor

Ansgar Steland is a Full Professor at the Institute of Statistics at RWTH Aachen University, Germany. Previously he held positions at the Technische Universität Berlin, the European University Viadrina and the Ruhr-University Bochum. He is an Elected Member of the International Statistical Institute (ISI), Chair of the Society for Reliability, Quality and Safety and Chair of the German Statistical Society’s Statistics in Natural Sciences and Technology Section. His main research interests include change detection and quality control, high-dimensional statistics, time series analysis, nonparametric statistics, and image analysis and its applications to econometrics, the natural sciences and engineering, especially photovoltaics. 

Kwok-Leung Tsui is a Professor at Virginia Tech’s Grado Department of Industrial and Systems Engineering in Blacksburg, VA, USA. Previously he held positions at AT&T Bell Laboratories, Georgia Institute of Technologyand the City University of Hong Kong. He is a fellow of the American Statistical Association, American Society for Quality, the International Society of Engineering Asset Management, and the Hong Kong Institution of Engineers. He is an elected Council Member of the International Statistical Institute (ISI), and a U.S. Representative to the International Organization for Standardization (ISO) Technical Committee on Statistical Methods. His current research interests include data science and data analytics, surveillance in healthcare and public health, personalized health monitoring, prognostics and systems health management, calibration and validation of computer models, process control and monitoring, and robust design and Taguchi methods.

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This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.