Introduction * Notation and Techniques * Representing Functional Data as Smooth Functions * The Roughness Penalty Approach * The Registration and Display of Functional Data * Principal Components Analysis for Functional Data * Regularized Principal Components Analysis * Principal Components Analysis of Mixed Data * Functional Linear Models * Functional Linear Models for Scalar Responses * Functional Linear Modesl for Functional Responses * Canonical Correlation and Discriminant Analysis * Differential Operators in Functional Data Analysis * Principal Differential Analysis * More General Roughness Penalties * Some Perspectives on FDA
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.
This is the second edition of a highly successful first edition. It contains a considerable amount of new material. Much of the work is original to the authors. Bernard Silverman has been very successful in writing books at a level and a style that appeals to theoretical and applied audiences.
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.
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.