250 citations · 489 across the 10 of their papers we have counts for
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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,…
Local asymptotic normality for discretely observed McKean-Vlasov diffusions
Akram Heidari, Mark Podolskij
We study the local asymptotic normality (LAN) property for the likelihood function associated with discretely observed -dimensional McKean-Vlasov stochastic differential equatio…
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…
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…
Polynomial rates via deconvolution for nonparametric estimation in McKean-Vlasov SDEs
Chiara Amorino, Denis Belomestny, VytautÄ PilipauskaitÄ +2
This paper investigates the estimation of the interaction function for a class of McKean-Vlasov stochastic differential equations. The estimation is based on observations of the as…