Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning the smoothness of noisy curves with application to online curve
  estimation   Combining information both within and across trajectories, we propose a
simple estimator for the local regularity of the trajectories of a stochastic
process. Independent trajectories are measured with errors at randomly sampled
time points. Non-asymptotic bounds for the concentration of the estimator are
derived. Given the estimate of the local regularity, we build a nearly optimal
local polynomial smoother from the curves from a new, possibly very large
sample of noisy trajectories. We derive non-asymptotic pointwise risk bounds
uniformly over the new set of curves. Our estimates perform well in
simulations. Real data sets illustrate the effectiveness of the new approaches.

27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph