Sup-norm adaptive drift estimation for multivariate nonreversible diffusions
C Aeckerle-Willems, C Strauch - The Annals of Statistics, 2022 - projecteuclid.org
The supplementary file contains the proofs of the presented results. With respect to
numbering, notation, and context it is written as a seamless continuation of the main article …
numbering, notation, and context it is written as a seamless continuation of the main article …
Adaptive invariant density estimation for continuous-time mixing Markov processes under sup-norm risk
N Dexheimer, C Strauch, L Trottner - … de l'Institut Henri Poincare (B …, 2022 - projecteuclid.org
Up to now, the nonparametric analysis of multidimensional continuous-time Markov
processes has focussed strongly on specific model choices, mostly related to symmetry of …
processes has focussed strongly on specific model choices, mostly related to symmetry of …
Parametric inference for ergodic McKean-Vlasov stochastic differential equations
V Genon-Catalot, C Larédo - Bernoulli, 2024 - projecteuclid.org
In this supplementary material, we detail an additional example which illustrates the
difference between the approach in Genon-Catalot and Larédo (2023) and the present …
difference between the approach in Genon-Catalot and Larédo (2023) and the present …
Fast convergence rates for estimating the stationary density in SDEs driven by a fractional Brownian motion with semi-contractive drift
We consider the solution of an additive fractional stochastic differential equation (SDE) and,
leveraging continuous observations of the process, introduce a methodology for estimating …
leveraging continuous observations of the process, introduce a methodology for estimating …
Mixing it up: A general framework for Markovian statistics
N Dexheimer, C Strauch, L Trottner - arXiv preprint arXiv:2011.00308, 2020 - arxiv.org
Up to now, the nonparametric analysis of multidimensional continuous-time Markov
processes has focussed strongly on specific model choices, mostly related to symmetry of …
processes has focussed strongly on specific model choices, mostly related to symmetry of …
Minimax rate of estimation for invariant densities associated to continuous stochastic differential equations over anisotropic Hölder classes
We study the problem of the nonparametric estimation for the density π π of the stationary
distribution of ad d‐dimensional stochastic differential equation (X t) t∈ 0, T\left (X _t\right) …
distribution of ad d‐dimensional stochastic differential equation (X t) t∈ 0, T\left (X _t\right) …
Rate of estimation for the stationary distribution of stochastic damping Hamiltonian systems with continuous observations
We study the problem of the non-parametric estimation for the density π of the stationary
distribution of a stochastic two-dimensional damping Hamiltonian system (Z t) t∈[0, T]=(X t …
distribution of a stochastic two-dimensional damping Hamiltonian system (Z t) t∈[0, T]=(X t …
Optimal convergence rates for the invariant density estimation of jump-diffusion processes
We aim at estimating the invariant density associated to a stochastic differential equation
with jumps in low dimension, which is for d= 1 and d= 2. We consider a class of fully non …
with jumps in low dimension, which is for d= 1 and d= 2. We consider a class of fully non …
Rate of estimation for the stationary distribution of jump-processes over anisotropic Holder classes
C Amorino - Electronic Journal of Statistics, 2021 - projecteuclid.org
We study the problem of the non-parametric estimation for the density π of the stationary
distribution of the multivariate stochastic differential equation with jumps (X t) 0≤ t≤ T, when …
distribution of the multivariate stochastic differential equation with jumps (X t) 0≤ t≤ T, when …
Nonparametric estimation of the stationary density for Hawkes-diffusion systems with known and unknown intensity
We investigate the nonparametric estimation problem of the density $\pi $, representing the
stationary distribution of a two-dimensional system $\left (Z_t\right) _ {t\in [0, T]}=\left …
stationary distribution of a two-dimensional system $\left (Z_t\right) _ {t\in [0, T]}=\left …