This volume covers the latest results on novel methods in Risk Analysis and assessment, with applications in Biostatistics (which is providing food for thought since the first ICRAs, covering traditional areas of RA, until now), Engineering Reliability, the Environmental Sciences and Economics. The contributions, based on lectures given at the 9th International Conference on Risk Analysis (ICRA 9), at Perugia, Italy, May 2022, detail a wide variety of daily risks, building on ideas presented at previous ICRA conferences. Working within a strong theoretical framework, supporting applications, the material describes a modern extension of the traditional research of the 1980s. This book is intended for graduate students in Mathematics, Statistics, Biology, Toxicology, Medicine, Management, and Economics, as well as quantitative researchers in Risk Analysis.

Covers new results in Risk Analysis and Assessment Focuses on applications in Biology, Engineering Reliability, Environmental Science Aims to promote interdisciplinary research and collaboration

Autorentext
Christos P. Kitsos obtained his PhD from U. of Glasgow in 1986. From 1991 to 2008 participated in a CCMS/NATO Pilot Study concerning Cancer Risk Assessment. Since 2015 is an Emeritus Professor of the U. of West Attica. In 2015 received an Exzellenzstiependium des Landes Oberosterreich at Johannes Kepler University. From 2022 to 2023 is a part time full professor in the Universidade Aberta, in Lisbon. He had published more than 160 papers in journals and refereed volumes (more than 1000 citations - Research Gate) and he is co- editor of some SPRINGER volumes in RA, and not only, the last 20 years. Teresa A. Oliveira is an Associate Professor with Habilitation, in Mathematics-Statistics, at the Department of Sciences and Technology of the Universidade Aberta in Lisbon. She obtained master's and doctoral degrees in Statistics and Operations Research at the University of Lisbon and is a senior member of the Center of Statistics and Applications (CEAUL) there. Her research interests include Experimental Design, Statistical Quality Control, Risk Analysis, Statistical Modeling, Computational Statistics, Stochastic Models, Data Analysis, and e-Learning Methodologies. She actively participated in numerous Erasmus+ Bilateral and Teaching Programs and was selected by the National Agency as an evaluator for Erasmus+ during 2021/2027. She presides over the ISI-CRA International Statistical Institute - Committee on Risk Analysis and was appointed as a founding member of the ISI Working Group on Data Science. With extensive editorial experience, she is an Associate Editor of JAS - Journal of Applied Statistics and of MPS - Mathematical Population Studies and is a member of other prestigious Journal Editorial Boards. She has authored/co-authored numerous peer-reviewed international and national papers, books, special issues, and proceedings, and her main research results were presented at many conferences, workshops, courses, and invited seminars worldwide. Her contributions to the field of Mathematics-Statistics continue to inspire new research and innovation, which is clearly reflected in the huge number of theses and dissertations under her supervision, in various courses, and in several universities.

Francesca Pierri graduated in Economics from the University of Perugia. Since 2005 she is Assistant Professor of Statistics at the Department of Economics, University of Perugia. Her primary research interest deals with failure prediction in economics and medical fields. The main statistical methods applied are logistic regression, logistic regression for unbalanced data, logistic regression for case-control studies, survival analysis, competing risks and multistate models. The results of her research activity have been published in international journals and have been presented in several international conferences and seminars held in foreign Universities. She serves as an Associate Editorof "Lifetime Data Analysis" and "Socio Economic Planning Science". Marialuisa Restaino is an Associate Professor of Statistics at the University of Salerno (Italy). She obtained her PhD from University of Salerno in 2008 and her Master Degree in Statistics from Lancaster University (UK) in 2007. Her teaching activity focuses on introductory statistics, computational statistics, and statistical modeling in bachelor's and master's degrees, and Ph.D. programs. Her research interests relate to survival analysis and multistate models, and the regression models in the presence of imbalanced datasets. Her methodological works are joined with applications for evaluating students' performance, and for predicting business failure. She had published 22 research journal articles, and 30 short papers published on special volume. She is a member of the Italian Statistical Society, and of the International Biometric Society - Italian Region.

Inhalt

• Examining the Network Effects in Bank Risk: Evidence from Liquidity Creation in Mutual Banks. - Teaching NoteData Science Training for Finance and Risk Analysis: A Pedagogical Approach with Integrating Online Platforms. - Analysing Misclassifications in Confusion Matrices. - Management Excellence Model Use: Brazilian Electricity Distributors Case. - A Statistical Boost to Assess Water Quality. - Time Series Procedures to Improve Extreme Quantile Estimation. - Factors Associated with Powerful Hurricanes in the Atlantic. - Reliable Alternative Ways to Manage the Risk of Extreme Events. - Risk Analysis in Practice and Theory. - On Some Consequences of COVID-19 in EUR/USD Exchange Rates and Economy. - Natural Risk Assessment of Italian Municipalities for Residential Insurance. - Variable Selection in Binary Logistic Regression for Modelling Bankruptcy Risk. - Operations with Iso-structured Models with Commutative Orthogonal Block Structure: An Introductory Approach. - Long and ShortRun Dynamics in Realized Covariance Matrices: A Robust MIDAS Approach. - Taxonomy-Based Risk Analysis with a Digital Twin. - Advanced Lattice Rules for Multidimensional Sensitivity Analysis in Air Pollution Modelling. - On Pitfalls in Statistical Analysis for Risk Assessment of COVID-19.