Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality

arXiv:2608.25468 · stat.ME, cs.LG, math.ST, stat.ML, stat.TH · Submitted 2026-08-26 · Read on arXiv

stat.ME, cs.LG, math.ST, stat.ML, stat.TH

Submitted: 2026-08-26

Updated: 2026-08-26

Comments: The Annals of Statistics

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Minimax optimality — 10× in this paper · explained in 2 episodes
Hilbert Space — 8× in this paper · explained in 2 episodes
Sparsity — 7× in this paper · explained in 4 episodes
Alignment — 5× in this paper · explained in 1 episode
Benchmark — 5× in this paper · explained in 2 episodes
Longitudinal Data — 5× in this paper · explained in 2 episodes
Reproducing Kernel Hilbert Space — 5× in this paper
Phase Transitions — 4× in this paper · explained in 2 episodes
Trace — 3× in this paper · explained in 2 episodes
Inverse Problem — 2× in this paper · explained in 2 episodes
Loss Function — 2× in this paper · explained in 2 episodes
Regularization — 2× in this paper · explained in 3 episodes
Reproducing Kernel Hilbert Space (RKHS) — 2× in this paper · explained in 3 episodes
Unified Framework — 2× in this paper · explained in 5 episodes
Accuracy — 1× in this paper · explained in 1 episode
Minimax Optimal Rates — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes
Theoretical Guarantees — 1× in this paper · explained in 2 episodes