Integral Inequality for Minimaxity and Characterization of Priors by Use of Inverse Laplace Transform
In the estimation of a multivariate normal mean, it is shown that the problem of deriving shrinkage estimators improving on the maximum likelihood estimator can be reduced to that of solving an integral inequality. The integral inequality not only provides a more general condition than a differential inequality studied in the literature, but also handles non-differentiable or discontinuous estimators. The paper also gives characterization of prior distributions such that the resulting Bayes equivariant or generalized Bayes estimators are minimax. This characterization is provided by using the inverse Laplace transform. Finally, a simple proof for constructing a class of estimators improving on the James-Stein estimator is given based on the integral expression of the risk.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||Jan 2006|
|Date of revision:|
|Contact details of provider:|| Postal: Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033|
Web page: http://www.cirje.e.u-tokyo.ac.jp/index.html
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:tky:fseres:2006cf393. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CIRJE administrative office)
If references are entirely missing, you can add them using this form.