Strang Splitting for Parametric Inference in Second-order Stochastic Differential Equations

TL;DR

This paper introduces a Strang splitting-based estimator for parameter inference in second-order stochastic differential equations, effectively handling hypoellipticity and partial observations, with proven consistency and asymptotic normality.

Contribution

It develops a novel Strang splitting estimator for second-order SDEs that addresses hypoellipticity and partial data, improving estimation accuracy and computational efficiency.

Findings

Full pseudo-likelihood reduces asymptotic variance with complete data.

Partial pseudo-likelihood yields less biased estimates with partial data.

Method successfully applied to paleoclimate data from Greenland ice cores.

Abstract

We address parameter estimation in second-order stochastic differential equations (SDEs), which are prevalent in physics, biology, and ecology. The second-order SDE is converted to a first-order system by introducing an auxiliary velocity variable, which raises two main challenges. First, the system is hypoelliptic since the noise affects only the velocity, making the Euler-Maruyama estimator ill-conditioned. We propose an estimator based on the Strang splitting scheme to overcome this. Second, since the velocity is rarely observed, we adapt the estimator to partial observations. We present four estimators for complete and partial observations, using the full pseudo-likelihood or only the velocity-based partial pseudo-likelihood. These estimators are intuitive, easy to implement, and computationally fast, and we prove their consistency and asymptotic normality. Our analysis demonstrates that using the full pseudo-likelihood with complete observations reduces the asymptotic variance of the diffusion estimator. With partial observations, the asymptotic variance increases as a result of information loss but remains unaffected by the likelihood choice. However, a numerical study on the Kramers oscillator reveals that using the partial pseudo-likelihood for partial observations yields less biased estimators. We apply our approach to paleoclimate data from the Greenland ice core by fitting the Kramers oscillator model, capturing transitions between metastable states reflecting observed climatic conditions during glacial eras.