Model-independent detection of new physics signals using interpretable SemiSupervised classifier tests-Reference-Cited by-同舟云学术

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Model-independent detection of new physics signals using interpretable SemiSupervised classifier tests

Published:2023-12-01 Issue:4 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Chakravarti Purvasha¹,Kuusela Mikael²,Lei Jing³,Wasserman Larry²

Affiliation:

1. Department of Statistical Science, University College London

2. Department of Statistics & Data Science, and NSF AI Planning Institute for Data-Driven Discovery in Physics, Carnegie Mellon University

3. Department of Statistics & Data Science, Carnegie Mellon University

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference76 articles.

1. Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J. and Wasserman, L. (2018). Distribution-free predictive inference for regression. J. Amer. Statist. Assoc. 113 1094–1111.

2. Breiman, L. (2001). Random forests. Mach. Learn. 45 5–32.

3. BACH, S., BINDER, A., MONTAVON, G., KLAUSCHEN, F., MÜLLER, K.-R. and SAMEK, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10 e0130140.

4. Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge Series in Statistical and Probabilistic Mathematics 1. Cambridge Univ. Press, Cambridge.

5. KIM, I., LEE, A. B. and LEI, J. (2019). Global and local two-sample tests via regression. Electron. J. Stat. 13 5253–5305.

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