A Spline-LASSO approach is proposed for a high-dimensional linear
regression problem, where the covariates are ordered in a meaningful
way. It is designed to tackle the case where the shape of the parameter
values changes smoothly, whereas fused Lasso is better fitted for
piecewise constant functions. Computationally it can be easily modified
to use LARS algorithms. Simulations and real example will be given.