Springer Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather

Presented in honor of Ursula Gather’s 60th birthday this book deals with modern topics in the field of robust statistical methods, especially for time series and regression analysis, and with statistical methods for complex data structures. From the Back Cover This Festschrift in honour of Ursula Gather’s 60th birthday deals with modern topics in the field of robust statistical methods, especially for time series and regression analysis, and with statistical methods for complex data structures. The individual contributions of leading experts provide a textbook-style overview of the topic, supplemented by current research results and questions. The statistical theory and methods in this volume aim at the analysis of data which deviate from classical stringent model assumptions, which contain outlying values and/or have a complex structure. Written for researchers as well as master and PhD students with a good knowledge of statistics. About the Author Prof. Dr. Claudia Becker is a Professor of Statistics at the Faculty of Law and Economics, Martin-Luther University Halle-Wittenberg. Her research priorities are robust statistical methods; robust statistical methods for dimension reduction in complex data structures; and statistical methods for regional economics to assess complex situations and relationships through interviews, with a particular focus on innovation and entrepreneurship research Prof. Dr. Roland Fried has been a Professor of Statistics in Biosciences at the Faculty of Statistics, Dortmund University, since 2006. His research focuses on biostatistics; modeling spatial and temporal data; online monitoring; robust signal extraction and structural break detection, and efficient statistical computational algorithms PD Dr. Sonja Kuhnt has held a temporary professorship at the Institute for Mathematical Statistics with Applications in Industry, Faculty for Statistics, TU Dortmund University, since 2008. Her present research fields are robust methods for categorical data and graphical models; computer algebra in statistics; offline planning of industrial processes; and acquisition of information in logistics