Binary Classification with the Maximum Score Model and Linear Programming

Joel L. Horowitz and Sokbae (Simon) Lee

Papers from arXiv.org

Abstract: This paper presents a computationally efficient method for binary classification using Manski's (1975, 1985) maximum score model when covariates are discretely distributed and parameters are partially but not point identified. We establish minimax-regret-optimal classification rules that take account of partial identification of the model's parameters. We bound misclassification probabilities and expected excess regret induced by sampling uncertainty. We also describe an extension of our method to continuous covariates. Our approach avoids the computational difficulty of maximum score estimation by reformulating the problem as two linear programs. Compared to parametric and nonparametric methods, our method balances extrapolation ability with minimal distributional assumptions. Monte Carlo simulations and empirical applications demonstrate its effectiveness and practical relevance.

Date: 2025-07, Revised 2026-08
New Economics Papers: this item is included in nep-dcm
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https://arxiv.org/pdf/2507.19654 Latest version (application/pdf)

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Working Paper: Binary classification with the maximum score model and linear programming (2025)
Working Paper: Binary classification with the maximum score model and linear programming (2025)
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