IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Robust Bayesian inference on scale parameters

We represent random variables Z that take values in Re^n-{0} as Z=RY, where R is a positive random variable and Y takes values in an (n-1)-dimensional space Y. By fixing the distribution of either R or Y, while imposing independence between them, different classes of distributions on Re^n can be generated. As examples, the spherical, l[q]-spherical, v-spherical and anisotropic classes can be interpreted in this unifying framework. We present a robust Bayesian analysis on a scale parameter in the pure scale model and in the regression model. In particular, we consider robustness of posterior inference on the scale parameter when the sampling distribution ranges over classes related to those mentioned above. Some links between Bayesian and sampling-theory results are also highlighted.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.econ.ed.ac.uk/papers/id25_esedps.pdf
Download Restriction: no

Paper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 25.

as
in new window

Length: 15
Date of creation: 1996
Date of revision:
Handle: RePEc:edn:esedps:25
Contact details of provider: Postal: 31 Buccleuch Place, EH8 9JT, Edinburgh
Phone: +44(0)1316508361
Fax: +44(0)1316504514
Web page: http://www.econ.ed.ac.uk/

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Gupta, A. K. & Song, D., 1997. "Characterization ofp-Generalized Normality," Journal of Multivariate Analysis, Elsevier, vol. 60(1), pages 61-71, January.
  2. Fernandez, C & Osiewalski, J & Steel, M-F-J, 1996. "Classical and Bayesian Inference Robustness in Multivariate Regression models," Papers 9602, Catholique de Louvain - Institut de statistique.
  3. Fang, Kai-Tai & Bentler, P. M., 1991. "A largest characterization of spherical and related distributions," Statistics & Probability Letters, Elsevier, vol. 11(2), pages 107-110, February.
  4. Fang, Kai-Tai & Li, Runze, 1999. "Bayesian Statistical Inference on Elliptical Matrix Distributions," Journal of Multivariate Analysis, Elsevier, vol. 70(1), pages 66-85, July.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:edn:esedps:25. 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: (Gina Reddie)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.