Robust Statistics through the Monitoring Approach: Applications in Regression

Atkinson, Anthony C.; Riani, Marco; Corbellini, Aldo; Perrotta, Domenico; Todorov, Valentin

ISBN 10: 3031883640 / ISBN 13: 9783031883644

Verlag: Springer, 2025

Beschreibung

Bestandsnummer des Verkäufers 26403765255

Bibliografische Details

• Titel: Robust Statistics through the Monitoring Approach: Applications in Regression
• Verlag: Springer
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-10: 3031883640
• ISBN-13: 9783031883644
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Extensions of the robust multiple regression model include Bayesian, heteroskedastic, time series and compositional regression, together with the clustering of regression models.

Über die Autorin bzw. den Autor

Anthony C. Atkinson is Emeritus Professor of Statistics at the London School of Economics and Political Science, UK. He is a former Joint Editor of the Journal of the Royal Statistical Society, Series B and Associate Editor of Biometrika. He is the author of more than 270 papers published in international journals of statistics and six books published by Springer, Oxford University Press and Chapman and Hall.
Marco Riani is a Full Professor of Statistics and the Director of the Robust Statistics Academy (Ro.S.A.), an interdepartmental center of the University of Parma, Italy. He is currently Associate Editor of Statistical Methods and Applications, Metron and STATS. He is the author of more than 200 publications and two books published by Springer. His research interests include robust statistics, regression, multivariate analysis and classification. 
Aldo Corbellini is an Associate Professor of Economic Statistics at the University of Parma, Italy. His research interests are in Bayesian robust regression and estimation, outlier detection in linear and generalized linear models and robust classification of functional data.
Domenico Perrotta is a Researcher at the Joint Research Centre of the European Commission in Ispra, Italy. He holds a PhD in the Computational Theory of Automatic Learning from the École Normale Supérieure de Lyon, France. His research interests span the interface between computer science and statistics, including robust methods and their application to regression and clustering problems in the domain of anti-fraud and trade data analysis.
Valentin Todorov worked as a Senior Management Information Officer at the United Nations Industrial Development Organization (UNIDO) in Vienna, Austria. He received a doctoral degree in statistics from Vienna University of Technology, Austria. His main research interests are in computational aspects of robust statistics, multivariate analysis, information systems and official statistics. He has (co-)developed and maintains several R packages on CRAN.