Pathwise least-squares estimator for linear SPDEs with additive fractional noise

TL;DR

This paper introduces a pathwise least-squares estimator for linear SPDEs with additive fractional noise, addressing stochastic integral challenges and analyzing its statistical and numerical properties.

Contribution

It develops a robust, pathwise least-squares estimator for linear SPDEs with fractional noise, including existence, uniqueness, and convergence analysis.

Findings

Estimator is strongly consistent.

Numerical solutions exist and are unique.

Conjectured asymptotic normality.

Abstract

This paper deals with the drift estimation in linear stochastic evolution equations (with emphasis on linear SPDEs) with additive fractional noise (with Hurst index ranging from 0 to 1) via least-squares procedure. Since the least-squares estimator contains stochastic integrals of divergence type, we address the problem of its pathwise (and robust to observation errors) evaluation by comparison with the pathwise integral of Stratonovich type and using its chain-rule property. The resulting pathwise LSE is then defined implicitly as a solution to a non-linear equation. We study its numerical properties (existence and uniqueness of the solution) as well as statistical properties (strong consistency and the speed of its convergence). The asymptotic properties are obtained assuming fixed time horizon and increasing number of the observed Fourier modes (space asymptotics). We also conjecture the asymptotic normality of the pathwise LSE.