Abstract
This note reconsiders, from a BAYESian viewpoint, the maximin approach to linear regression estimation with restricted parameter space adopted by KRAFFT [4]. It is shown that krafft's maximin turns out to be minimax whenever there exists a degenerate (one-point) measure which is least favourable among all prior distributions on the parameter space.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.