A Nonparametric Mean Estimator for Judgment Poststratified Data
Article first published online: 28 JUN 2008
© 2008, The International Biometric Society
Volume 64, Issue 2, pages 355–363, June 2008
How to Cite
Wang, X., Lim, J. and Stokes, L. (2008), A Nonparametric Mean Estimator for Judgment Poststratified Data. Biometrics, 64: 355–363. doi: 10.1111/j.1541-0420.2007.00900.x
- Issue published online: 28 JUN 2008
- Article first published online: 28 JUN 2008
- Received November 2006. Revised July 2007. Accepted July 2007.
- Imperfect ranking;
- Imprecise ranking;
- Isotonic regression;
- Multiple rankers;
- Ranked set sampling;
- Simple stochastic ordering
Summary MacEachern, Stasny, and Wolfe (2004, Biometrics60, 207–215) introduced a data collection method, called judgment poststratification (JPS), based on ideas similar to those in ranked set sampling, and proposed methods for mean estimation from JPS samples. In this article, we propose an improvement to their methods, which exploits the fact that the distributions of the judgment poststrata are often stochastically ordered, so as to form a mean estimator using isotonized sample means of the poststrata. This new estimator is strongly consistent with similar asymptotic properties to those in MacEachern et al. (2004). It is shown to be more efficient for small sample sizes, which appears to be attractive in applications requiring cost efficiency. Further, we extend our method to JPS samples with imprecise ranking or multiple rankers. The performance of the proposed estimators is examined on three data examples through simulation.