The Theory of Decision Procedures for Distributions with Monotone Likelihood Ratio

By S. Karlin, H. Rubin. 1956

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Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “This notion of fairness is connected to utility maximization by a set of results showing that, for a certain set of utility functions and scores ψ(v), the utility-maximizing decision rule δ is necessarily a single-threshold rule [68, 7, 19, 82].”
Comparative Statics, Informativeness, and the Interval Dominance Order (2009, unknown): “GE – p. 12/36”
Admissible treatment rules for a risk-averse planner with experimental data on an innovation (2007, unknown): “The results of Karlin and Rubin (1956) show that a strong form of this conjecture is correct if f(@)”
Asymptotics for Statistical Treatment Rules (2009, methodology): “This result is modelled after the essential complete class theorem of Karlin and Rubin (1956), which applies to models with a scalar parameter satisfying the monotone likelihood ratio property.”
Monotone Comparative Statics under Uncertainty (2002, background): “ Karlin and Rubin (1956) establish sufficiency, and Karlin (1968) (pp.”
Statistical treatment rules for heterogeneous populations (2003, methodology): “Considering the classical normal linear regression model, Kempthorne (1984) finds that all variable-selection procedures are admissible.”
Statistical treatment choice based on asymmetric minimax regret criteria (2009, background): “In problem (5) the planner gives  times greater weight to regret from Type I errors.”
Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “…of fairness is connected to utilitymaximization by a set of results showing that, for a certain set of utility functions and scores ψ(v), the utility-maximizing decision rule δ is necessarily a single-threshold rule (Karlin & Rubin 1956, Berger 1985, Corbett-Davies et al. 2017, Lipton et al. 2018).”