Marie Hušková – författare

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.

This volume gathers peer-reviewed contributions presented at the 6th International Workshop on Functional and Operatorial Statistics, IWFOS 2025, held in Novara, Italy, June 25-27, 2025. 

Covering a broad spectrum of topics in functional and operatorial statistics and related fields, including high-dimensional statistics and machine learning, the contributions tackle both fundamental theoretical challenges and practical applications. A variety of features of statistics for functional data are addressed, such as estimation of functional features, exploration and pre-processing of functional data, methodologies for functional regression and forecasting problems, unsupervised and supervised classification, and testing procedures. Nonstandard functional data and situations which go beyond the pattern of samples of independent variables are investigated, and a link to the field of artificial intelligence is presented. Interesting real data applications to medicine, health, economics and the natural, environmental and social sciences are featured throughout.

Initiated at the University of Toulouse in 2008, the series of IWFOS workshops fosters discussion and international collaboration on theoretical advancements, methodological innovations, and applications in functional and operatorial statistics and related fields.

Chapter 42 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.