Aim & Scope
Statistical Inference for Stochastic Processes will be devoted to the following topics: Parametric semiparametric and nonparametric inference in discrete and continuous time stochastic processes (especially: ARMA type processes diffusion type processes point processes random fields Markov processes). Analysis of time series. Spatial Models. Empirical Processes. Applications to finances insurances economics biology physics and engineering. [1]
2024
Quasi-maximum likelihood estimation of long-memory linear processes
Statistical Inference for Stochastic Processes , 2024
Viking: variational Bayesian variance tracking
Statistical Inference for Stochastic Processes , 2024
Nonparametric estimation of the diffusion coefficient from i.i.d. S.D.E. paths
Statistical Inference for Stochastic Processes , 2024
Nonparametric spectral density estimation under local differential privacy
Statistical Inference for Stochastic Processes , 2024
A model specification test for nonlinear stochastic diffusions with delay
Statistical Inference for Stochastic Processes , 2024
A pseudo-likelihood estimator of the Ornstein–Uhlenbeck parameters from suprema observations
No authors listed.
Statistical Inference for Stochastic Processes , 2024