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Full Description
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.
Contents
- Examining the Network Effects in Bank Risk: Evidence from Liquidity Creation in Mutual Banks. - Teaching Note—Data 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 Short-Run 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.
