Scientists today collect samples of curves and other functional observations. This monograph presents many ideas and techniques for such data. Included are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drwan from growth analysis, meterology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, to applied data analysts, and to experienced researchers; it will have value both within statistics and across a broad spectrum of other fields. Much of the material is based on the authors' own work, some of which appears here for the first time. Jim Ramsay is Professor of Psychology at McGill University and is an international authority on many aspects of multivariate analysis. He draws on his collaboration with researchers in speech articulation, motor control, meteorology, psychology, and human physiology to illustrate his technical contributions to functional data analysis in a wide range of statistical and application journals. Bernard Silverman, author of the highly regarded "Density Estimation for Statistics and DataAnalysis," and coauthor of "Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach," is Professor of Statistics at Bristol University. His published work on smoothing methods and other aspects of applied, computational, and theoretical statistics has been recognized by the Presidents' Award of the Committee of Presidents of Statistical Societies, and the award of two Guy Medals by the Royal Statistical Society.

This is the second edition of a highly succesful book which has sold nearly 3000 copies world wide since its publication in 1997.

Many chapters will be rewritten and expanded due to a lot of progress in these areas since the publication of the first edition.

Bernard Silverman is the author of two other books, each of which has lifetime sales of more than 4000 copies. He has a great reputation both as a researcher and an author.

This is likely to be the bestselling book in the Springer Series in Statistics for a couple of years.

What do juggling, old bones, criminal careers and human growth patterns have in common? They all give rise to functional data, that come in the form of curves or functions rather than the numbers, or vectors of numbers, that are considered in conventional statistics. The authors' highly acclaimed book Functional Data Analysis (1997) presented a thematic approach to the statistical analysis of such data. By contrast, the present book introduces and explores the ideas of functional data analysis by the consideration of a number of case studies, many of them presented for the first time. The two books are complementary but neither is a prerequisite for the other. The case studies are accessible to research workers in a wide range of disciplines. Every reader, whether experienced researcher or graduate student, should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. Some of the studies demand the development of novel aspects of the methodology of functional data analysis, but technical details aimed at the specialist statistician are confined to sections which the more general reader can safely omit. There is an associated web site with MATLAB and S-PLUS implementations of the methods discussed, together with all the data sets that are not proprietary. Jim Ramsay is Professor of Psychology at McGill University, and is an international authority on many aspects of multivariate analysis. He was elected President of the Statistical Society of Canada for the term 2002-3 and is a holder of the Society's Gold Medal for his work in functional data analysis. His statistical work draws on his collaborations with researchers in speech articulation, biomechanics, economics, human biology, meteorology and psychology. Bernard Silverman is Professor of Statistics at Bristol University. He was President of the Institute of Mathematical Statistics in 2000-1 and has held various offices in the Royal Statistical Society. He is a Fellow of the Royal Society and a member of Academia Europaea. His main specialty is computational statistics, and he is the author or editor of several highly regarded books in this area. He has also published widely in theoretical and applied statistics, and in many other fields, including law, human and veterinary medicine, earth sciences and engineering.

Wavelets are transforming current thinking in a wide range of fields by allowing for intermittent information and non- homogeneous behaviour. This book examines their increasing use and potential in many areas, including physical systems, turbulence, statistics, mechanical engineering, neural networks, physiology, vision engineering, signal processing, economics and astronomy. It is a must for specialists and non specialists alike.