Hypotheses testing about the drift parameter in linear stochastic differential equation driven by stable processes

DOI10.1007/S10260-015-0335-6zbMath1441.62222OpenAlexW1266384182WikidataQ115384936 ScholiaQ115384936MaRDI QIDQ333541

David Stibůrek

Publication date: 31 October 2016

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-015-0335-6

zbMATH Keywords

Wiener processinverse methodshypotheses testinglinear stochastic differential equationsymmetric stable Lévy process

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Asymptotic properties of parametric estimators (62F12) Markov processes: estimation; hidden Markov models (62M05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Stable stochastic processes (60G52)

Uses Software

• sde

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Robust parameter estimation for the Ornstein-Uhlenbeck process
• Parameter estimation for a class of stochastic differential equations driven by small stable noises from discrete observations
• Least squares estimator for discretely observed Ornstein-Uhlenbeck processes with small Lévy noises
• Parameter estimation for fractional Ornstein-Uhlenbeck processes
• On the singularity of least squares estimator for mean-reverting \(\alpha\)-stable motions
• On Itô stochastic integration with respect to p-stable motion: Inner clock, integrability of sample paths, double and multiple integrals
• The consistency of a nonlinear least squares estimator from diffusion processes
• Tests with parabolic boundary for the drift of a Wiener process
• Some time change representations of stable integrals, via predictable transformations of local martingales
• Sequential variational testing hypotheses on the Wiener process under delayed observations
• Martingale estimation functions for discretely observed diffusion processes
• Drift estimation of a certain class of diffusion processes from discrete observation
• Least squares estimator for Ornstein-Uhlenbeck processes driven by \(\alpha \)-stable motions
• Simulation and inference for stochastic differential equations. With R examples.
• Rates of weak convergence of approximate minimum contrast estimators for the discretely observed Ornstein-Uhlenbeck process
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• Statistical inference on the drift parameter in fractional Brownian motion with a deterministic drift
• On effects of discretization on estimators of drift parameters for diffusion processes
• Lévy Processes and Stochastic Calculus
• A stochastic integral in storage theory
• Rates of convergence of approximate maximum likelihood estimators in the Ornstein-Uhlenbeck process
• Accuracy of normal approximation for the maximum likelihood estimator and Bayes estimators in the Ornstein-Uhlenbeck process using random normings

This page was built for publication: Hypotheses testing about the drift parameter in linear stochastic differential equation driven by stable processes