New Frontiers in Statistics and Data Science: SPE2023, Guimarães, Portugal, October 11-14: 469 (Springer Proceedings in Mathematics & Statistics, 469) - Tapa dura

Acerca del autor

Lígia Henriques-Rodrigues is an Assistant Professor in the Mathematics Department at the School of Science and Technology, University of Évora (Portugal), and a researcher at the Center of Mathematics and Applications, Institute for Research and Advanced Training at the same institution. Lígia is currently an elected member of the board of directors of the Portuguese Statistical Society (2024–2026). She has collaborated as a referee for several international peer-reviewed journals. Her main research interests are statistics of extremes, nonparametric statistics, and computational statistical methods, with applications in life sciences, environment, risk, insurance, and finance.

Raquel Menezes is an Associate Professor with Habilitation in the Department of Mathematics, University of Minho, Portugal and a member of its Centre of Mathematics. She is an invited researcher at the Centre of Statistics and its Applications (CEAUL), Lisbon University. Her main research interests are spatial and temporal statistics, non-parametric estimation, species distribution modelling and their applications. She has acted as principal investigator of projects funded by the Portuguese National Funding Agency for Science, Research and Technology. Since 2019, she has collaborated with the Department of the Sea and Marine Resources of IPMA. In the last 12 years, she has supervised 6 PhD students and published about 34 papers in international peer-reviewed journals.

Luís Meira Machado is a Full Professor in the Department of Mathematics at the University of Minho, Portugal, and a researcher at its Centre of Mathematics. His research interests encompass various aspects of survival analysis, with a particular focus on multi-state models, nonparametric estimation, ROC curves, generalized additive models, and computational statistics. More recently, he has extended his focus to the application of these methodologies within the domain of machine learning. With over 53 journal articles and book chapters, he has made significant contributions, including advancements in multi-state models and computational statistics, showcased by the creation of 12 packages in the CRAN R repository. Luís is the current President of the Portuguese Statistical Society.

Miguel de Carvalho is the Chair of Statistical Data Science at the University of Edinburgh and the Co-Director of the Edinburgh Centre for Financial Innovations. His eclectic research interests include applied statistics, biostatistics, econometrics, risk analysis, and statistics of extremes. Miguel has served as an Associate Editor for top-tier journals such as The American Statistician, The Annals of Applied Statistics, and the Journal of the American Statistical Association. He is an Elected Fellow of the International Statistical Institute and holds an honorary appointment as Invited Full Professor at Universidade de Aveiro.

Susana Faria is an Associate Professor in the Department of Mathematics at the University of Minho, Portugal and a researcher at its Centre of Mathematics. Her main scientific areas of interest include regression models, mixture models, and mathematical modeling. She has collaborated as a referee for several international peer-reviewed journals. Her publications are in statistics and its applications, with an emphasis on life and health sciences, engineering, and social sciences.

De la contraportada

This volume showcases a collection of thirty-two peer-reviewed articles presented at the XXVI Congress of the Portuguese Statistical Society (2023). It covers a wide range of cutting-edge topics in both theoretical and applied statistics. Each contribution highlights the latest advancements and research in the field, offering valuable insights and innovative methodologies for researchers and practitioners alike. Readers with a background in mathematics and statistics will find it particularly beneficial, while researchers from various scientific disciplines can explore numerous robust applications.