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Invidious Comparisons: Ranking and Selection as Compound Decisions

Jiaying Gu, Roger Koenker

arXiv 23 Dec 2020 · Econometrics · publishedEconometrica (2023) · 35 citations (OpenAlex)

arXiv:2012.12550 · PDF · DOI · OpenAlex · Extracted main text

Abstract

There is an innate human tendency, one might call it the "league table mentality," to construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are ranked even though their inherent multi-dimensionality would suggest that -- at best -- only partial orderings were possible. We consider a large class of elementary ranking problems in which we observe noisy, scalar measurements of merit for $n$ objects of potentially heterogeneous precision and are asked to select a group of the objects that are "most meritorious." The problem is naturally formulated in the compound decision framework of Robbins's (1956) empirical Bayes theory, but it also exhibits close connections to the recent literature on multiple testing. The nonparametric maximum likelihood estimator for mixture models (Kiefer and Wolfowitz (1956)) is employed to construct optimal ranking and selection rules. Performance of the rules is evaluated in simulations and an application to ranking U.S kidney dialysis centers.

Citation extraction

65
references
83
in-text mentions
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distinct cited
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Efron and Morris (1973) Stein's Estimation Rule and Its Competitiors - An Empirical Bayes Approach0.92843100%
2Henderson and Newton (2016) Making the cut: improved ranking and selection for large-scale inference0.7373367%
3Gilraine, Gu, and McMillan (2020) A New Method for Estimating Teacher Value-Added0.73732100%
4Polyanskiy and Wu (2020) Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models0.73732100%
5Chetty, Friedman, and Rockoff (2014) Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates0.64422100%
6Chetty, Friedman, and Rockoff (2014) Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood0.64422100%
7Jiang and Zhang (2021) Compound Empirical Bayes Interval Estimation0.64422100%
8Kiefer and Wolfowitz (1956) Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters0.64422100%
9Lin, Louis, Paddock, and Ridgeway (2006) Loss Function Based Ranking in Two-Stage, Hierarchical Models0.64422100%
10Robbins (1956) An Empirical Bayes Approach to Statistics0.64422100%

Showing the top 10 of 65 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Quasi-Bayes in Latent Variable Models0.92844
2Bayesian ranking and selection with applications to field studies, economic mobility, and forecasting0.81142
3Empirical Bayes Selection for Value Maximization0.81142
4Compound Selection Decisions: An Almost SURE Approach0.64422
5Nonparametric Empirical Bayes Confidence Intervals0.64422
6Partially Identified Rankings from Pairwise Interactions0.58531
7A Discrimination Report Card0.51121
8Finite- and Large-Sample Inference for Ranks using Multinomial Data with an Application to Ranking Political Parties0.51121
9A model of multiple hypothesis testing0.40511
10Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis0.40511