Publication Results

Code: 09/2015
Title: On linear regression models in infinite dimensional spaces with scalar response
Date: Wednesday 11th February 2015
Author(s) : Ghiglietti, A.; Ieva, F.; Paganoni, A.m.; Aletti, G.
Download link:
Abstract: In functional linear regression, the parameters estimation involves solving a non necessarily well-posed problem and it has points of contact with a range of methodologies, including statistical smoothing, deconvolution and projection on finite-dimensional subspaces. We discuss the standard approach based explicitly on functional principal components analysis, nevertheless the choice of the number of basis components remains something subjective and not always properly discussed and justified. In this work we discuss inferential properties of least square estimation in this context with different choices of projection subspaces, as well as we study asymptotic behaviour increasing the dimension of subspaces.

This report, or a modified version of it, has been also submitted to, or published on
Accepted for publication: "Statistical Papers" doi: 10.1007/s00362-015-0710-2. (2015)