# Drift Estimation for Stochastic Reaction-Diffusion Systems

## TL;DR

This paper develops methods for estimating parameters in stochastic reaction-diffusion systems, providing conditions for consistency and asymptotic normality, along with robustness results under model uncertainty.

## Contribution

It introduces new estimation techniques and theoretical results specifically tailored for stochastic reaction-diffusion systems, including robustness under model uncertainty.

## Key findings

- Conditions for consistency established
- Asymptotic normality proven for estimators
- Robustness results demonstrated

## Abstract

A parameter estimation problem for a class of semilinear stochastic evolution equations is considered. Conditions for consistency and asymptotic normality are given in terms of growth and continuity properties of the nonlinear part. Emphasis is put on the case of stochastic reaction-diffusion systems. Robustness results for statistical inference under model uncertainty are provided.