Skip Navigation

Biometrika 1993 80(4):797-806; doi:10.1093/biomet/80.4.797
© 1993 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by GODAMBE, V. P.
Right arrow Articles by KUNTE, S.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Optimal estimation under biased allocation of treatments

V. P. GODAMBE1 and S. KUNTE2

1Department of Statistics and Actuarial Science, University of Waterloo Waterloo, Ontario, N2L 3G1, Canada
2Department of Statistics, University of Poona Pune-411007, India

This paper provides a semi-parametric solution to a problem proposed by Robbins & Zhang (1991). The solution is obtained utilizing the theory of optimum estimating functions (Godambe & Thompson, 1989). Our solution coincides with maximum likelihood estimation for the parametric model suggested by Robbins & Zhang (1991). Even for alternative models investigated in this paper, the efficiency of our estimation appears very close to that of maximum likelihood estimation.

Key Words: Biased allocation • Efficiency • Estimating functions • Optimality • Posterior mean


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.