Measuring dependence is a basic question when dealing with functional observations. The usual correlation for curves is not robust. Kendall's coefficient is a natural description of dependence between finite dimensional random variables. We extend this concept to functional observations. Given a...

We present a notion of Spearman's coefficient for functional data that extends the classical bivariate concept to situations where the observed data are curves generated by a stochastic process. Since Spearman's coefficient for bivariate samples is based on the natural data ordering in dimension...

In a health context dependency is defined as lack of autonomy in performing basic activities of daily living that require the care of another person or significant help. However, this contingency, if present, changes throughout the lifetime. In fact, empirical evidence shows that, once this...

A popular approach for classifying functional data is based on the distances from the function or its derivatives to group representative (usually the mean) functions or their derivatives. In this paper, we propose using a combination of those distances. Simulation studies show that our...

Functional data are difficult to manage for many traditional statistical techniques given their very high (or intrinsically infinite) dimensionality. The reason is that functional data are essentially functions and most algorithms are designed to work with (low) finite-dimensional vectors....