Analysis of Ordinal Populations from Judgment Post-Stratification

Amirhossein Alvandi and Armin Hatefi ()
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Amirhossein Alvandi: Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA
Armin Hatefi: Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada

Stats, 2023, vol. 6, issue 3, 1-27

Abstract: In surveys requiring cost efficiency, such as medical research, measuring the variable of interest (e.g., disease status) is expensive and/or time-consuming; however, we often have access to easily obtainable characteristics about sampling units. These characteristics are not typically employed in the data collection process. Judgment post-stratification (JPS) sampling enables us to supplement the random samples from the population of interest with these characteristics as ranking information. This paper develops methods based on the JPS samples for estimating categorical ordinal populations. We develop various estimators from the JPS data even for situations where the JPS suffers from empty strata. We also propose the JPS estimators using multiple ranking resources. Through extensive numerical studies, we evaluate the performance of the methods in estimating the population. Finally, the developed estimation methods are applied to bone mineral data to estimate the bone disorder status of women aged 50 and older.

Keywords: judgment post-stratification; ordinal variable; maximum likelihood estimation; non-parametric method; isotonic model; multiple rankers; empty strata; ordinal logistic regression (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2023
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