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Mixing it up: A general framework for Markovian statistics
v1v2v3 (latest)

Mixing it up: A general framework for Markovian statistics

Annales De L Institut Henri Poincare-probabilites Et Statistiques (Ann. IHP Probab. Stat.), 2020
31 October 2020
Niklas Dexheimer
Claudia Strauch
Lukas Trottner
ArXiv (abs)PDFHTML

Papers citing "Mixing it up: A general framework for Markovian statistics"

7 / 7 papers shown
Statistical algorithms for low-frequency diffusion data: A PDE approach
Statistical algorithms for low-frequency diffusion data: A PDE approachAnnals of Statistics (Ann. Stat.), 2024
Matteo Giordano
Sven Wang
292
9
0
02 May 2024
Data-driven rules for multidimensional reflection problems
Data-driven rules for multidimensional reflection problems
Soren Christensen
Asbjorn Holk Thomsen
Lukas Trottner
214
8
0
11 Nov 2023
Adaptive nonparametric drift estimation for multivariate jump diffusions
  under sup-norm risk
Adaptive nonparametric drift estimation for multivariate jump diffusions under sup-norm riskStochastic Processes and their Applications (SPA), 2023
Linqi Zhou
302
0
0
29 Sep 2023
Malliavin calculus for the optimal estimation of the invariant density
  of discretely observed diffusions in intermediate regime
Malliavin calculus for the optimal estimation of the invariant density of discretely observed diffusions in intermediate regime
Chiara Amorino
A. Gloter
313
2
0
05 Aug 2022
Estimating the characteristics of stochastic damping Hamiltonian systems
  from continuous observations
Estimating the characteristics of stochastic damping Hamiltonian systems from continuous observationsStochastic Processes and their Applications (SPA), 2021
Niklas Dexheimer
Claudia Strauch
321
3
0
27 Sep 2021
Learning to reflect: A unifying approach for data-driven stochastic
  control strategies
Learning to reflect: A unifying approach for data-driven stochastic control strategies
Soren Christensen
Claudia Strauch
Lukas Trottner
212
13
0
23 Apr 2021
Optimal convergence rates for the invariant density estimation of
  jump-diffusion processes
Optimal convergence rates for the invariant density estimation of jump-diffusion processes
Chiara Amorino
Eulalia Nualart
272
10
0
21 Jan 2021
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