Abstract
Two dimensional reduction regression methods to predict a scalar response from a discretized sample path of a continuous time covariate process are presented. The methods take into account the functional nature of the predictor and are both based on appropriate wavelet decompositions. Using
such decompositions, prediction methods are devised that are similar to minimum average variance estimation (MAVE) or functional sliced inverse regression (FSIR). Their practical implementation is described, together with their application both to simulated and on real data analyzing three calibration examples of near infrared spectra.
Anno
2006
Autori IAC
Tipo pubblicazione
Altri Autori
Amato U.; Antoniadis A.; De Feis I.
Editore
Elsevier Science
Rivista
Computational statistics & data analysis (Print)