Curve Linear Regression via Dimension Reduction
A new methodology for linear regression with both curve response
and curve regressors, which is described in Cho, Goude, Brossat and Yao
(2013) and (2015)
. The key idea behind this methodology is
dimension reduction based on a singular value decomposition in a Hilbert
space, which reduces the curve regression problem to several scalar linear
regression problems.