Journal of Applied Mathematics and Decision Sciences
Volume 2007 (2007), Article ID 86180, 23 pages
Research Article

On the Semiparametric Efficiency of the Scott-Wild Estimator under Choice-Based and Two-Phase Sampling

Alan Lee

Department of Statistics, University of Auckland, Auckland 1142, New Zealand

Received 30 April 2007; Accepted 8 August 2007

Academic Editor: Paul Cowpertwait

Copyright © 2007 Alan Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Using a projection approach, we obtain an asymptotic information bound for estimates of parameters in general regression models under choice-based and two-phase outcome-dependent sampling. The asymptotic variances of the semiparametric estimates of Scott and Wild (1997, 2001) are compared to these bounds and the estimates are found to be fully efficient.