Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10525010 | Journal of Statistical Planning and Inference | 2005 | 15 Pages |
Abstract
The convergence rates of empirical Bayes estimation in the exponential family are studied in this paper. We first develop an approach for obtaining the lower bound of empirical Bayes estimators. As an application of the approach, we demonstrate that O(nâ1) is the lower bound rate for priors with bounded compact support. Second, we construct an empirical Bayes estimator using kernel sequence method and show that it has a rate of convergence of O(nâ1(lnn)8). This upper bound rate is much faster compared to the earlier results published in the literature under the same assumption.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Jianjun Li, Shanti S. Gupta, Friedrich Liese,