Functional Data Analysis (Second Edition) (Facsimile Edition)

Author: J.O. Ramsay
Publisher:
Publication Date: 2006-01-17
Features: Today, scientists collect curve samples and other functional observations. This monograph discusses the ideas and techniques for analyzing such data, primarily including classical linear regression methods, principal component analysis, linear modeling, canonical correlation analysis, and special functional techniques such as curve registration and principal differential analysis. The book consistently uses data from real-world applications to introduce the motivation behind the methods and provide illustrative examples. It particularly demonstrates how functional methods can reveal new characteristics of data by discussing the smoothness of the data-generating process. These data primarily come from applications in fields such as growth analysis, meteorology, biomechanics, equine science, economics, and medicine. The book presents innovative statistical techniques while ensuring that the mathematical arguments are accessible to a broad audience. Much of the content is based on the author's own work, with some material being published for the first time. This book is suitable for students, applied data analysis scholars, and researchers, and it holds significant value for research in statistics and other broad fields.
The author, Jim Ramsay, is a professor of psychology at McGill University, the president of the Statistical Society of Canada, and an international authority in multiple analysis and other fields. He has collaborated with researchers in various domains, including speech clarity, electric control, meteorology, psychology, and human physiology, publishing numerous papers in journals on statistics and its applications. His contributions have significantly advanced the field of functional data analysis.
The co-author, Bernard Silverman, is a professor of statistics at the University of Bristol and the author of the well-known book Density Estimation and Data Analysis in Statistics. He is also a co-author of Nonparametric Regression and Generalized Linear Models. Recognized for his work in smoothing methods, applied statistics, computational statistics, and theoretical statistics, he has been awarded the President's Medal by the Royal Statistical Society Council and two Guy medals from the Royal Statistical Society for his contributions.