Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science

This book presents a unique collection of contributions on modern methods and applications in three key areas of statistics, celebrating the significant work of Wolfgang Schmid in this field. It is structured thematically into parts focusing on statistical process monitoring, financial statistics, and spatial statistics with environmetrics, each featuring chapters from leading experts.

The opening articles on statistical process monitoring present novel methodologies for the detection of anomalies and control charting techniques, which are crucial for maintaining quality in manufacturing processes. Detailed discussions are included on integrating multivariate statistical methods and real-time monitoring to enhance process reliability and efficiency.

The part on financial statistics explores rigorous approaches in financial econometrics, with an emphasis on dynamic modelling of market volatility and risk assessment. Contributions cover advanced asset allocation strategies, leveraging high-dimensional data analysis, and the application of machine learning techniques.

Spatial statistics and environmetrics are addressed through innovative research on the statistical analysis of environmental data. This includes the use of geostatistical models and hybrid models that combine traditional statistical techniques with machine learning to improve the prediction of environmental phenomena. Key topics here involve the modelling of extremes and airborne pollutants, the prediction of earthquakes using a smartphone-based sensor network, and reviews of selected topics essential in modern spatial statistics.

Each part not only reflects Wolfgang Schmid’s interests and impact in these areas but also provides detailed theoretical and applied studies, demonstrating how these sophisticated statistical methods can be effectively employed in practical scenarios. This makes the book an indispensable resource for researchers and practitioners looking to apply cutting-edge statistical techniques in these complex fields.

Sven Knoth has been a Professor of Computational Statistics at the Helmut Schmidt University, the University of the Federal Armed Forces in Hamburg, Germany since 2009. He obtained his PhD at the TU Chemnitz in 1995 and after serving as a postdoc at the Department of Biostatistics, Heinrich Heine University in Düsseldorf, he switched to the European University Viadrina in Frankfurt (Oder), where he completed his postdoctoral studies, and worked as Senior Process Engineer and SPC Coordinator at the Advanced Mask Technology Center (AMTC) in Dresden. His main research areas are statistical process control, implementing statistical algorithms in software, and applying statistics in the engineering world. He is currently an Associate Editor of the journal Computational Statistics.

Yarema Okhrin is Professor of Statistics and Data Science at the University of Augsburg, Germany. He teaches financial econometrics, data science, machine learning, and multivariate data analysis. His research focuses on multivariate statistics and econometrics, and machine learning with applications to finance, statistical surveillance and computational statistics. He previously worked as Assistant Professor of Econometrics at the University of Bern and at the European University Viadrina. He is the Co-Editor-in-Chief of AStA Advances in Statistical Analysis.

Philipp Otto is a Professor of Statistics and Data Analytics at the School of Mathematics and Statistics, University of Glasgow, UK. Previously, he was an Associate Professor (Reader) at the same university, as well as an Assistant Professor of Big Geospatial Data at the Institute of Cartography and Geoinformatics at Leibniz University Hannover and the interim Professor of Spatial Data Analysis and Statistical Learning at the University of Göttingen. Before that, he led the junior research group Detection and Surveillance of Spatial and Spatiotemporal Clusters at the Viadrina Center B/ORDERS IN MOTION at the European University in Frankfurt (Oder), where he also obtained his Ph.D. in Statistics in 2016. His research focuses on statistical data science and statistical methodology for data in multidimensional spaces, including geo-referenced data, spatiotemporal statistics, statistical learning, environmetrics, statistical process control and network modelling. He is actively involved in the scientific community as an Associate Editor of three journals.