Springer Functional and High-Dimensional Statistics and Related Fields (Contributions to Statistics)

Product Description This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field. From the Back Cover This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field. About the Author Germán Aneiros is an Associate Professor of Statistics at the University of A Coruña, Spain. His research focuses on statistical inference for functional data, including sparse semi-parametric regression models, selection of impact points in a curve, bootstrap procedures and functional prediction of electricity demand and price. He is an Associate Editor of the journal Computational Statistics. Ivana Horová is a Full Professor of Applied Mathematics at Masaryk University, Brno, Czech Republic. Her research focuses on nonparametric statistical methods, particularly multivariate kernel smoothing and its applications. She is a co-author of a monograph on kernel smoothing in MATLAB. She was a Guest Editor of the special issue Computational Environmetrics in the journal Environmetrics in 2009. Marie Hušková is a Full Professor of Mathematical Statistics at Charles University, Prague, Czech Republic. She is the author of more than 130 scientific papers, mainly on asymptotic statistics, nonparametric and multivariate statistics and change-point problems. She is an Associa