Models for Longitudinal Data with Applications to Early Warning Systems: Springer Proceedings in Mathematics & Statistics, cartea 553

Francesco Bartolucci is a Full Professor of Statistics at the University of Perugia, fellow of the Institute of Mathematical Statistics, and Editor of Statistical Modelling: An International Journal. His research is in statistics and econometrics, focusing mainly on latent variable models, in particular hidden Markov models, for complex data with applications in different fields, ranging from economics to health.
 Paolo Li Donni is Full Professor of Public Economics at the University of Palermo. His research lies at the intersection of econometrics, public finance, and health economics. It combines applied microeconometrics with structural and Bayesian approaches to study healthcare demand, provider competition, and the organization of hospital and primary-care networks.
 Fulvia Pennoni is Full Professor of Statistics at the University of Milano-Bicocca. Her research focuses on latent variable models, including hidden Markov models, with applications in social sciences, health, and finance. She is the author of numerous international scientific publications and two books in statistical modelling.
 Claudia Pigini is Associate Professor of Econometrics at the Marche Polytechnic University. Her research focuses on panel data models, with applications in several areas of economics, including labor economics, household finance, the economics of innovation, and banking. She is the author of international publications in microeconometrics and applied economics.

Chapter 1 Sampling-based and cost-sensitive classification in early warning systems for financial crises.- Chapter 2 Auto Machine Learning for Early Warning Crisis Detection.- Chapter 3 Exploring Binary Regression and Hidden Markov Models for Early Warning Systems.- Chapter 4 A regularized EWS for banking crises: a grouped fixed effects approach.- Chapter 5 A Bayesian Student’s t-Hidden Markov Model Approach for Cryptocurrencies Time Series.- Chapter 6 Link prediction in temporal networks: A dynamic stochastic block model approach.- Chapter 7 The substitution between primary and emergency care in individuals with chronic conditions: evidence from a structural model.- Chapter 8 The demand of primary and secondary care: a Bayesian hierarchical approach.