S681 | 27589 | Karen Kafadar

This course will introduce students to methods for analyzing functional data --- i.e., data that are entire curves, rather than single observations or vectors of several measurements. Such data objects involve numerous highly-related points per object, and the methods for analyzing them make explicit use of data objects as functions. In contrast to multivariate analysis (multiple values per object that different degrees of associations) and longitudinal analysis or "panel studies" (where measurements are repeated on an individual at only a few time points), the methods here treat the object as a function. We discuss these methods which include graphical displays, summaries, analogs of conventional analyses (analysis of variance, principal components), with an emphasis on applications. The two required books will address both these goals (methodology and applications). The course is valuable for both data researchers whose data are entire functions (gait, spectra, etc.) as well as students interested in participating in a relatively new and important area of statistical research. Required textbooks: James Ramsay and Bernard Silverman, Functional Data Analysis (FDA), Springer. James Ramsay and Bernard Silverman, Applied Functional Data Analysis (AFDA), Springer.