Judgment Post-stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example

This paper presents novel estimators for a judgment post-stratified (JPS) sample, which combine the ranking information from different methods or rankers. A JPS sample divides the units in the original simple random sample (SRS) into several ranking groups based on the relative positions (ranks) of the units in their individual small comparison sets. Ranks in the comparison sets may be assigned with several different ranking procedures. When considered separately, each ranking method leads to a different JPS sample estimator of the population mean or total. Here we introduce equally or unequally weighted estimators, which combine the ranking information from multiple sources. The unequal weights utilize the standard errors of the individual ranking methods estimators. The weighted estimators provide a substantial improvement over an SRS estimator and a JPS estimator based on a single ranking method. The new estimators are applied to crop establishment phenotypic data from an agricultural field experiment. Supplementary materials accompanying this paper appear online.