Abstract:
We consider the problem of estimating an infinite-dimensional vector $\theta$ observed in Gaussian white noise. Under the condition that components of the vector have a Gaussian prior distribution that depends on an unknown parameter $\beta$, we construct an adaptive estimator with respect to $\beta$. The proposed method of estimation is based on the empirical Bayes approach.
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