Clustering techniques for functional data are reviewed. Four groups of clustering algorithms for functional data are … consists of distance-based methods using clustering algorithms based on specific distances for functional data. A software …

reduction and inference for functional data. The selection of the number, d, of the FPC’s to be used in a specific procedure has … functional data can be sufficiently well approximated by a projection onto a finite-dimensional subspace, and the error resulting … increasing number of the FPC’s. Such an approach reflects more fully the infinite-dimensional nature of functional data, and …

important vehicle and reference point for the development and evaluation of robust methods in functional data analysis. In this …

In recent years, several methods have been proposed to deal with functional data classification problems (e.g., one … for classifying functional data. This wavelet-thresholding semi-metric is able to adapt to the smoothness of the data and …

the integrated periodograms are curves, we apply functional data depth-based techniques to make the classification robust …

nearby time points. Functional data analysis methodologies have been developed to solve these type of problems. In order to … predict the class membership (multi-category response variable) associated to an observed curve (functional data), a …

parameters of a normal distribution are discussed, together with its relationship to functional data analysis and other standard …

, multivariate, panel and functional data. We also present real data examples which illustrate the utility of the surveyed results …

We explore the use of principal differential analysis as a tool for performing dimensional reduction of functional data … and effective representation of functional data, easily interpretable in terms of exponential, sinusoidal, or damped …-sinusoidal functions and providing a different insight to the functional data set under investigation. Moreover, in the analysis of the …