# Robust Fusion Methods for Big Data

## TL;DR

This paper explores robust fusion techniques to combine estimators from subsamples in Big Data, aiming to improve estimation accuracy when handling large datasets without processing the entire sample.

## Contribution

It introduces novel robust fusion methods specifically designed for Big Data scenarios where full data processing is infeasible.

## Key findings

- Effective fusion procedures that enhance estimator robustness.
- Improved estimation accuracy in large-scale data settings.
- Method demonstrates scalability and robustness in experiments.

## Abstract

We address one of the important problems in Big Data, namely how to combine estimators from different subsamples by robust fusion procedures, when we are unable to deal with the whole sample.