Robust Drift Parameter Estimation In Diffusion Processes
نویسنده
چکیده
We consider some inference problems concerning the drift parameters vector of diffusion process. Namely, we consider the case where the parameters vector is suspected to satisfy certain restriction. Under such a design and imprecise prior information, we propose Stein-rule (or shrinkage) estimators which improves over the performance of the classical maximum likelihood estimator (MLE). By using the asymptotic distributional quadratic risk criterion, their relative dominance is addressed. Simulation results confirm that that shrinkage estimators provide excellent estimation accuracy and outperform the MLE uniformly.
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