Abstract
“Similar curves” in the present article refers to a family of curves whose major shape are similar, but who have variation coming from curve-specific sources. The goal here is to develop a general methodology to describe small changes among similar curves. Previous methods mainly focus on dimension reduction through FPCA, which are not appropriate for quantifying local variation. Here, we consider a local functional data model which divides data into segments adaptively and models each segment with a shape invariant model. Such model has great flexibility in characterizing local variation of curves, as illustrated by simulation and real data examples.
| Original language | English |
|---|---|
| Pages (from-to) | 4745-4759 |
| Number of pages | 15 |
| Journal | Communications in Statistics Part B: Simulation and Computation |
| Volume | 46 |
| Issue number | 6 |
| DOIs | |
| State | Published - 3 Jul 2017 |
Keywords
- Functional data
- Knot selection
- Shape invariant model