In many statistical applications data are curves measured as functions of a
continuous parameter as time. Despite of their functional nature and due to discrete time observation, these type of data are usually analyzed with multivariate statistical
methods that do not take into account the high correlation between observations of a
single curve at 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 functional generalized logit model is proposed. Base-line category logit formula-
tions will be considered and their estimation based on basis expansions of the sample
curves of the functional predictor and parameters. Functional principal component
analysis will be used to get an accurate estimation of the functional parameters and
to classify sample curves in the categories of the response variable. The good performance of the proposed methodology will be studied by developing an experimental
study with simulated and real data.Projects MTM2010-20502 from Dirección General de Investigación del MEC SpainFQM-08068 from Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía Spai
