Robust smoothed canonical correlation analysis for functional data

11/20/2020
by   Graciela Boente, et al.
0

This paper provides robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by using robust association and scale measures combined with basis expansion and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/31/2021

Intrinsic Wasserstein Correlation Analysis

We develop a framework of canonical correlation analysis for distributio...
research
10/19/2019

Robustifying multiple-set linear canonical analysis with S-estimator

We consider a robust version of multiple-set linear canonical analysis o...
research
11/03/2020

Canonical Correlation Analysis in high dimensions with structured regularization

Canonical Correlation Analysis (CCA) is a technique for measuring the as...
research
08/10/2021

Γ-convergence of Onsager-Machlup functionals. Part II: Infinite product measures on Banach spaces

We derive Onsager-Machlup functionals for countable product measures on ...
research
08/22/2023

Computational Inference for Directions in Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) is a method for analyzing pairs of ...
research
03/01/2021

Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data

It is quite common for functional data arising from imaging data to assu...
research
09/17/2019

BLOCCS: Block Sparse Canonical Correlation Analysis With Application To Interpretable Omics Integration

We introduce Block Sparse Canonical Correlation Analysis which estimates...

Please sign up or login with your details

Forgot password? Click here to reset