Treffer: Adaptive Bayes type estimators of ergodic diffusion processes from discrete observations.

We consider adaptive Bayesian estimation of both drift and diffusion coefficient parameters for ergodic multidimensional diffusion processes based on sampled data. Under a general condition on the discretization step of the sampled data, three kinds of adaptive Bayes type estimators are proposed by applying adaptive maximum likelihood type methods of Uchida and Yoshida (Stoch Process Appl 122:2885-2924, ) to Bayesian procedures. We show asymptotic normality and convergence of moments for the adaptive Bayes type estimators by means of the Ibragimov-Has'minskii-Kutoyants program together with the polynomial type large deviation inequality for the statistical random field. [ABSTRACT FROM AUTHOR]

Copyright of Statistical Inference for Stochastic Processes is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)