Nonparametric Functional Data Analysis: Theory and Practice Frederic Ferraty and Philippe Vieu is an English-language textbook aimed at functional data and nonparametric functional data analysis. This book provides an in-depth theoretical framework, combined with practical methods for analyzing functional data.
It covers fundamental aspects of functional nonparametric modeling, including the mathematical foundations, the development of statistical techniques, and their asymptotic properties. In addition, considerable attention is given to computational tools with R and S-PLUS routines. Practical examples from chemometrics, econometrics, and pattern recognition demonstrate the broad applicability of functional data analysis across various scientific disciplines.
This edition effectively connects theory and practice and is therefore suitable for both academic researchers and professionals working with functional data. The accompanying dataset and codes support the learning process and practical applications.
Topics align with fields such as Probability and Statistics and Stochastics, fitting within the Springer Series in Statistics.