Inference in generalized exponential O–U processes

In this paper, we consider an inference problem in generalized exponential Ornstein–Uhlenbeck processes. Salient features of this paper consists in the fact that, first, we generalized the classical exponential Ornstein–Uhlenbeck processes to the case where the drift coefficient is driven by a perio...

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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 2023-10, Vol.26 (3), p.581-618
Hauptverfasser: Lyu, Yunhong, Nkurunziza, Sévérien
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description In this paper, we consider an inference problem in generalized exponential Ornstein–Uhlenbeck processes. Salient features of this paper consists in the fact that, first, we generalized the classical exponential Ornstein–Uhlenbeck processes to the case where the drift coefficient is driven by a period function of time. Second, as opposed to the results in recent literature, the dimension of the drift parameter is considered as unknown. Third, we weaken some assumptions, in recent literature, underlying the asymptotic optimality of some estimators of the drift parameter. We propose the unrestricted maximum likelihood estimator, the restricted maximum likelihood estimator and some shrinkage estimators for the drift parameters. We also derive asymptotic distributional risk of the proposed estimators as well as their relative efficiency. Finally, we present the simulation results which corroborate the theoretical findings.
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subjects Asymptotic properties
Drift
Inference
Mathematics
Mathematics and Statistics
Maximum likelihood estimators
Parameters
Probability Theory and Stochastic Processes
Statistical Theory and Methods
title Inference in generalized exponential O–U processes
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