Abstract
Let X be an N p (o, ∑) random vector. Suppose besides n observations on X, m observations on the first q( q < p) coordinates are available. Eaton (1970), for this set up, has given a minimax estimator of ∑, which is better than the MLE. We, in this paper, obtain a class of constant risk minimax estimators (Eaton's estimator is its member), and hence estimators better than any member of this class. Similar results are derived also for the estimation of ∑-1. The loss functions considered are those of Selliah (1964) and James and Stein (1961) for the estimation of ∑ and an analogue of Stein's loss function for the estimation of ∑-1.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
