Dynamic principal component analysis from a global perspective
From MaRDI portal
Publication:6632622
DOI10.3150/24-bej1743MaRDI QIDQ6632622
Publication date: 5 November 2024
Published in: Bernoulli (Search for Journal in Brave)
Statistics (62-XX) Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Multivariate analysis (62Hxx)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A gradient-descent method for curve fitting on Riemannian manifolds
- Geometric foundations for scaling-rotation statistics on symmetric positive definite matrices: minimal smooth scaling-rotation curves in low dimensions
- Optimal estimation of the mean function based on discretely sampled functional data: phase transition
- Principal component analysis.
- A two-step algorithm of smooth spline generation on Riemannian manifolds
- A New Geometric Algorithm to Generate Smooth Interpolating Curves on Riemannian Manifolds
- Bayesian and geometric subspace tracking
- UNROLLING SHAPE CURVES
- Solving ODEs with MATLAB
- Fitting Smooth Paths to Speherical Data
- Smoothing Splines on Riemannian Manifolds, with Applications to 3D Shape Space
- Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
- Intrinsic Regression Models for Positive-Definite Matrices With Applications to Diffusion Tensor Imaging
- Dynamic Functional Principal Components
- Local polynomial regression for symmetric positive definite matrices
- Functional Data Analysis for Sparse Longitudinal Data
- Lie groups beyond an introduction
- On lines and planes of closest fit to systems of points in space.
- Dynamic Principal Component Analysis in High Dimensions
This page was built for publication: Dynamic principal component analysis from a global perspective
