3 papers
math.ST2026
Weighted Nuclear Elastic Net Estimation of (Near-) Low-Rank Drift Matrices in Ornstein-Uhlenbeck Processes
Dmytro Marushkevych, Francisco Pina, Mark Podolskij
We study estimation of the drift matrix in a continuously observed high-dimensional Ornstein-Uhlenbeck process when the drift is exactly or approximately low rank. In this setting,…
math.ST2025
Sampling effects on Lasso estimation of drift functions in high-dimensional diffusion processes
Chiara Amorino, Francisco Pina, Mark Podolskij
In this paper, we address high-dimensional parametric estimation of the drift function in diffusion models, specifically focusing on a -dimensional ergodic diffusion process obs…
math.ST2025
Consistent support recovery for high-dimensional diffusions
Dmytro Marushkevych, Francisco Pina, Mark Podolskij
Statistical inference for stochastic processes has advanced significantly due to applications in diverse fields, but challenges remain in high-dimensional settings where parameters…