### Functional data analysis (FDA)

Functional data analysis (FDA) handles longitudinal data and treats

each observation as a function of time (or other variable). The

functions are related. The goal is to analyze a sample of functions

instead of a sample of related points.

FDA differs from traditional data analytic techniques in a number of

ways. Functions can be evaluated at any point in their domain.

Derivatives and integrals, which may provide better information (e.g.

graphical) than the original data, are easily computed and used in

multivariate and other functional analytic methods.

S+Functional Data Analysis User's Guide

by Douglas B. Clarkson, Chris Fraley, Charles C. Gu, James O. Ramsay

Functional Data Analysis (Springer Series in Statistics) (Hardcover)

by J. Ramsay, B. W. Silverman

Covers topics of linear models, principal components, canonical

correlation, and principal differential analysis in function spaces.

Applied Functional Data Analysis (Paperback)

by J.O. Ramsay, B.W. Silverman

Bernard W. Silverman's code site Applied Functional Data Analysis: Methods and Case Studies