Estimation of a Mean of a Normal Distribution with a Bounded Coefficient of Variation
The estimation of a mean of a normal distribution with an unknown variance is addressed under the restriction that the coefficient of variation is within a bounded interval. The paper constructs a class of estimators improving on the best location-scale equivariant estimator of the mean. It is demonstrated the class includes three typical estimators: the Bayes estimator against the uniform prior over the restricted region, the Bayes estimator against the prior putting mass on the boundary, and a truncated estimator. The non-minimaxity of the best location-scale equivariant estimator is shown in the general location-scale family. When another type of restriction is treated, however, we have a different story that the best location-scale equivariant estimator remains minimax.
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:||Oct 2004|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.cirje.e.u-tokyo.ac.jp/index.htmlEmail:
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:tky:fseres:2004cf306. 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.