Stochastic Models, Statistics and Their Applications (eBook)

Dresden, Germany, March 2019

Ansgar Steland, Ewaryst Rafajłowicz, Ostap Okhrin (Herausgeber)

eBook Download: PDF

2019 | 1st ed. 2019
XVI, 450 Seiten
Springer International Publishing (Verlag)
978-3-030-28665-1 (ISBN)

Infos

This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance. 

Ansgar Steland received his Ph.D. in Mathematics from the University of Göttingen, Germany. After positions at the Technische Universität Berlin as a consultant, at the European University Viadrina and the Ruhr-University of Bochum, he joined the faculty of RWTH Aachen University, Germany, where he was appointed Full Professor at the Institute of Statistics in 2006. 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 are in 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.

Ewaryst Rafaj?owicz received his Ph.D. and D.Sc. degrees in Control Theory from Wroc?aw University of Technology, Poland. In 1996 he became a Full Professor, and in 2016 he was elected to the Polish Academy of Sciences as a Corresponding Member. He has been a Visiting Professor at many universities in the USA, Canada, Germany and England and has published ca. 150 papers on the identification of distributed-parameter systems, optimal design of experiments, signal processing, neural networks, nonparametric regression estimation, statistical quality control and image processing. In addition, he has served on the program committees of several international conferences and as a reviewer for many journals.

Ostap Okhrin is a Professor of Econometrics and Statistics at the Technische Universität Dresden, Germany. He worked at the European University Viadrina and later was an Assistant and then Associate Professor for Statistics of Financial Markets at the Humboldt-Universität zu Berlin and one of the principal investigators of the CRC-649 (Collaborative Research Center) 'Economic Risk.' He currently teaches multivariate, computational and mathematical statistics, and his research focuses on multivariate models, in particular in copulas and financial econometrics.

Produkt-Details

• Erscheint lt. Verlag: 15.10.2019
• Reihe/Serie: Springer Proceedings in Mathematics & Statistics
• Reihe/Serie: Springer Proceedings in Mathematics & Statistics
• Zusatzinfo: XVI, 450 p. 85 illus., 46 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Wirtschaft
• Schlagworte: algorithms • Big Data • change points and detection • Copulas • Data Science • Econometrics • High-Dimensional Statistics • machine learning • nonparameteric estimation • random fields • reliability theory • Simulations • Statistics for Stochastic Processes • stochastic models • Survival Analysis
• ISBN-10: 3-030-28665-7 / 3030286657
• ISBN-13: 978-3-030-28665-1 / 9783030286651