toc
next
last
Download Citation
Recommend
Mark
Alert
Link
Print
Linear and ellipsoidal restrictions in linear regression
Authors:
P. Stahlecker a;
G. Trenkler a
| Affiliation: | a Department of Economics, University of Mainz, Mainz, Germany |
DOI:
10.1080/03610929108802299
Publication Frequency:
6 issues per year
Subjects:
Mathematical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
Formats available:
PDF
(English)
Article Requests:
Order Reprints
:
Request Permissions
-
Single Article Purchase:
US$37.00
-
buy now
-
Sign In
- Online Sample
add to cart 
[ show other buying options ]
View Article:
View Article (PDF)
Abstract
The problem of combining linear and ellipsoidal restrictions in linear regression is investigated. Necessary and sufficient conditions for compactness of the restriction set are proved assuring the existence of a minimax estimator. When the restriction set is not compact a minimax estimator may still exist for special loss functions arid regression designs
|
| Keywords: Linear regression model; prior information; minimax estimation; restricted parameter space; quadratic loss functions |
| view references (11) : view citations |
|
Bookmark with:
|
CiteULike
Del.icio.us
BibSonomy
Connotea
More bookmarks
