Asymptotic normality of discretized maximum likelihood estimator for drift parameter in homogeneous diffusion model

DOI10.15559/15-VMSTA21zbMath1349.62072arXiv1506.01810MaRDI QIDQ340755

Kostiantyn Ralchenko

Publication date: 15 November 2016

Published in: Modern Stochastics. Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.01810

zbMATH Keywords

stochastic differential equationasymptotic normalitydrift parameterdiscretized model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Diffusion processes (60J60)

Related Items (1)

Adaptive control of diffusion processes with a discounted reward criterion

Uses Software

• sde

Cites Work

• Estimation of diffusion parameters for discretely observed diffusion processes
• Estimation for diffusion processes from discrete observation
• Quasi-likelihood and its application. A general approach to optimal parameter estimation
• Statistical inference for ergodic diffusion processes.
• Standard maximum likelihood drift parameter estimator in the homogeneous diffusion model is always strongly consistent
• Estimation of the coefficients of a diffusion from discrete observations
• Statistical Methods for Stochastic Differential Equations
• Approximate discrete-time schemes for statistics of diffusion processes
• Parametric Inference for Discretely Sampled Stochastic Differential Equations
• Estimation for the discretely observed telegraph process
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