In this paper, a novel parameter estimation method based on a two-stage neural network
(PENN) is proposed to carry out a joint estimation of a parameterized stochastic differential …

This work proposes a data-driven parameter identification approach for a two-degree-of-
freedom airfoil system with cubic nonlinearity and stochasticity, where the random turbulent …

Abstract Let Z:={Z t, t≥ 0} be a stationary Gaussian process. We study two estimators of E [Z
0 2], namely f ˆ T (Z):= 1 T∫ 0 TZ t 2 dt, and f˜ n (Z):= 1 n∑ i= 1 n Z ti 2, where ti= i Δ n, i= 0 …

Assuming that a threshold Ornstein–Uhlenbeck process is observed at discrete time
instants, we propose generalized moment estimators to estimate the parameters. Our …

Assuming that a reflected Ornstein–Uhlenbeck process is observed at discrete time instants,
we propose generalized moment estimators to estimate all the drift and diffusion parameters …

The goal of this paper is to construct ergodic estimators for double exponential Ornstein–
Uhlenbeck process, where the process is observed at discrete time instants with time step …

Uncertain differential equations, as an alternative to stochastic differential equations, have
proved to be extremely powerful across various fields, especially in finance theory. The …

Low-frequency observations are a common occurrence in real-world applications, making
statistical inference for stochastic processes driven by stochastic differential equations …

Mathematical models for complex systems under random fluctuations often certain uncertain
parameters. However, quantifying model uncertainty for a stochastic differential equation …

Asymptotic theory for explosive fractional Ornstein-Uhlenbeck processes Page 1 Electronic
Journal of Statistics Vol. 18 (2024) 3931–3974 ISSN: 1935-7524 https://doi.org/10.1214/24-EJS2293 …