Advances in functional regression and classification models

Resumen

Functional data analysis (FDA) has become a very active field of research in the last few years because it appears naturally in most scientific fields: energy (electricity price curves), environment (curves of pollutant levels), chemometrics (spectrometric data), etc. This thesis is a compendium of the following publications: 1) "Statistical computing in functional data analysis: the R package fda.usc" published in the Journal of Statistical Software, the core advances of this paper was to propose a common framework in R for FDA. 2) "Predicting seasonal influenza transmission using functional regression models (FRM) with temporal dependence" published in PLoS ONE proposes an extension of GLS model to functional case. 3) "The DDG-classifier in the functional setting" published in TEST extends the DD-classifier using information derived of the depth of FD. 4) "Determining optimum wavelengths for leaf water content estimation from reflectance: A distance correlation approach" published in Chemometrics and Intelligent Laboratory Systems studies the utility of distance correlation as a method to select impact points in FR. 5) "Variable selection in Functional Additive Regression Models", in minor revisión in COST, proposes a variable selection algorithm in the case of scalar, multivariate o functional predictors.