@inproceedings{b0474b05a6da4b6a96b41f81001feea7,
title = "Achievable Error Exponents for Almost Fixed-Length M-Ary Hypothesis Testing",
abstract = "We revisit multiple hypothesis testing and propose a two-phase test, where each phase is a fixed-length test and the second-phase proceeds only if a reject option is decided in the first phase. We derive achievable error exponents of error probabilities under each hypothesis and show that our two-phase test bridges over fixed-length and sequential tests in both Neyman-Pearson and Bayesian settings in the similar spirit of Lalitha and Javidi [1] for binary hypothesis testing. Specifically, our test may achieve the performance close to a sequential test with the asymptotic complexity of a fixed-length test and such test is named the almost fixed-length test. Our results generalize the design and analysis of the almost fixed-length test for binary hypothesis testing to account for more than two outcomes.",
keywords = "Bayesian, Hypothesis Testing, Large deviations, Neyman-Pearson, Two-phase test",
author = "Jun Diao and Lin Zhou and Lin Bai",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10095947",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "美国",
}