Hybrid estimation for ergodic diffusion processes based on noisy discrete observations

Hybrid estimation for ergodic diffusion processes based on noisy discrete observations

We consider parametric estimation for ergodic diffusion processes with noisy sampled data based on the hybrid method, that is, the multi-step estimation with the initial Bayes type estimators in order to select proper initial values for optimisation of the quasi likelihood function. The asymptotic p...

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Veröffentlicht in:Statistical inference for stochastic processes : an international journal devoted to time series analysis and the statistics of continuous time processes and dynamic systems 2020-04, Vol.23 (1), p.171-198
Hauptverfasser: Kaino, Yusuke, Nakakita, Shogo H., Uchida, Masayuki
Format: Artikel
Sprache:eng
Schlagworte:
Asymptotic properties
Ergodic processes
Estimating techniques
Estimators
Mathematics
Mathematics and Statistics
Optimization
Parameter estimation
Probability Theory and Stochastic Processes
Statistical Theory and Methods
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Zusammenfassung:We consider parametric estimation for ergodic diffusion processes with noisy sampled data based on the hybrid method, that is, the multi-step estimation with the initial Bayes type estimators in order to select proper initial values for optimisation of the quasi likelihood function. The asymptotic properties of the initial Bayes type estimators and the hybrid multi-step estimators are shown, and a concrete example and the simulation results are given.
ISSN:1387-0874
1572-9311
DOI:10.1007/s11203-019-09203-2