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  "Title": "Argmin Inference over a Discrete Candidate Set",
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  "Description": "Provides methods to construct frequentist confidence sets\nwith valid marginal coverage for identifying the\npopulation-level argmin or argmax based on IID data. For\ninstance, given an n by p loss matrix—where n is the sample\nsize and p is the number of models—the CS.argmin() method\nproduces a discrete confidence set that contains the model with\nthe minimal (best) expected risk with desired probability. The\nargmin.HT() method helps check if a specific model should be\nincluded in such a confidence set. The main implemented method\nis proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024)\n\"Winners with confidence: Discrete argmin inference with an\napplication to model selection\".",
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  "Date/Publication": "2025-07-09 02:21:52 UTC",
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