Springer Functional Data Analysis (Springer Series in Statistics)

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.