Minimum density power divergence estimator for diffusion processes

DOI10.1007/S10463-012-0366-9zbMath1441.62219OpenAlexW1987188402MaRDI QIDQ1934487

Sangyeol Lee, Junmo Song

Publication date: 28 January 2013

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-012-0366-9

zbMATH Keywords

robustnessOrnstein-Uhlenbeck processdiffusion processesminimum density power divergence estimatordiscretely observed sample

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Robustness and adaptive procedures (parametric inference) (62F35) Markov processes: estimation; hidden Markov models (62M05) Diffusion processes (60J60)

Related Items (4)

Robust parametric inference for finite Markov chains ⋮ General Robust Bayes Pseudo-Posteriors: Exponential Convergence Results with Applications ⋮ Robust estimation in stochastic frontier models ⋮ Robust test for dispersion parameter change in discretely observed diffusion processes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Minimum distance density-based estimation
• Estimation for diffusion processes from discrete observation
• On the estimation of the diffusion coefficient for multi-dimensional diffusion processes
• Minimum Hellinger distance estimates for parametric models
• Statistical inference for ergodic diffusion processes.
• Minimum disparity estimation for continuous models: Efficiency, distributions and robustness
• Test for parameter change based on the estimator minimizing density-based divergence meas\-ures
• Estimation of the coefficients of a diffusion from discrete observations
• Approximate discrete-time schemes for statistics of diffusion processes
• Minimum Hellinger Distance Estimation for Multivariate Location and Covariance
• Minimum Hellinger Distance Estimation for the Analysis of Count Data
• Robust and efficient estimation by minimising a density power divergence
• Estimation of an Ergodic Diffusion from Discrete Observations
• Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach

This page was built for publication: Minimum density power divergence estimator for diffusion processes