One of the most difficult problems in cluster analysis is identifying the number of groups in a
dataset. Most previously suggested approaches to this problem are either somewhat ad hoc …

… been used previously in a functional classification setting (James and Hastie 2001). In the fi
nite… We use an alternative approach suggested by Sugar and James (2003) based on the “dis…

The elements of a multivariate dataset are often curves rather than single points. Functional
principal components can be used to describe the modes of variation of such curves. If one …

We present a technique for extending generalized linear models to the situation where
some of the predictor variables are observations from a curve or function. The technique is …

We introduce a technique for extending the classical method of linear discriminant analysis (LDA)
to data sets where the predictor variables are curves or functions. This procedure, …

Regression models to relate a scalar Y to a functional predictor X(t) are becoming increasingly
common. Work in this area has concentrated on estimating a coefficient function, β(t), with …

When using squared error loss, bias and variance and their decomposition of prediction error
are well understood and widely used concepts. However, there is no universally accepted …

We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig
selector with a similar computational cost to the least angle regression algorithm that is used to …

… James and Silverman [23] proposed an index model to implement a nonlinear functional
regression, and, more recently, both [14] and [7] extended this work to a fully nonparametric …

… We use the “jump” approach (Sugar and James 2003) to select the optimal number of …
Sugar and James (2003) show through the use of information theory and simulations that setting …