argminCS: Argmin Inference over a Discrete Candidate Set
Provides methods to construct frequentist confidence sets with valid marginal
coverage for identifying the population-level argmin or argmax based on IID data.
For instance, given an n by p loss matrix—where n is the sample size and p is the
number of models—the CS.argmin() method produces a discrete confidence set that contains
the model with the minimal (best) expected risk with desired probability. The argmin.HT()
method helps check if a specific model should be included in such a confidence set. The main
implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024)
"Winners with confidence: Discrete argmin inference with an application to model selection".
| Version: |
1.1.0 |
| Imports: |
BSDA, glue, LDATS, MASS, methods, Rdpack, stats, withr |
| Published: |
2025-07-14 |
| DOI: |
10.32614/CRAN.package.argminCS |
| Author: |
Tianyu Zhang [aut],
Hao Lee [aut, cre, cph],
Jing Lei [aut] |
| Maintainer: |
Hao Lee <haolee at andrew.cmu.edu> |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/xu3cl4/argminCS |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
argminCS results |
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