Resistant estimates for high dimensional and functional data based on random projections

TL;DR

This paper introduces a new robust estimation method utilizing random projections, designed for high-dimensional and functional data, which is adaptive, computationally efficient, and maintains robustness under contamination.

Contribution

The paper presents a novel random projection-based robust estimation technique that is adaptive, efficient, and suitable for high or infinite dimensional data.

Findings

Method is robust under contamination models

Achieves full efficiency in simulations and real data

Eases computation for high-dimensional data

Abstract

We herein propose a new robust estimation method based on random projections that is adaptive and, automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.