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.

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Summary We consider the problem of estimating the number of distinct species S in a study area from the recorded presence or absence of species in each of a sample of quadrats. A generalized jackknife estimator of S is derived, along with an estimate of its variance. It is compared with the jackknife estimator for S proposed by Heltshe and Forrester (1983, Biometrics39, 1–12) and the empirical Bayes estimator of Mingoti and Meeden (1992, Biometrics48, 863–875). We show that the empirical Bayes estimator has the form of a generalized jackknife estimator under a specific model for species distribution. We compare the new estimators of S to the empirical Bayes estimator via simulation. We characterize circumstances under which each is superior.

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AbstractJudge performance is a critical component of a wine competition's success. A number of studies have shown that wine judges may differ considerably in their opinions. In this paper, we have conducted an in-depth examination of wine judge performance at a U.S. wine competition. Three characteristics of judge's performance are examined: bias, discrimination ability, and variation. Based on the analysis, we can identify the judges who had discrepant scoring patterns and can gain insight into which of the three characteristics cause particular judges to disagree. The evaluation of wine judge performance through these three characteristics may provide useful information for training them to have consistent performance and in assisting competition coordinators in judge selection. (JEL Classification: C1, D8, Q13)