Large Deviation Analysis of Score-based Hypothesis Testing
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914665951395840 |
|---|---|
| author | Diao, Enmao Banerjee, Taposh Tarokh, Vahid |
| author_facet | Diao, Enmao Banerjee, Taposh Tarokh, Vahid |
| contents | Score-based statistical models play an important role in modern machine learning, statistics, and signal processing. For hypothesis testing, a score-based hypothesis test is proposed in \cite{wu2022score}. We analyze the performance of this score-based hypothesis testing procedure and derive upper bounds on the probabilities of its Type I and II errors. We prove that the exponents of our error bounds are asymptotically (in the number of samples) tight for the case of simple null and alternative hypotheses. We calculate these error exponents explicitly in specific cases and provide numerical studies for various other scenarios of interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15519 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Deviation Analysis of Score-based Hypothesis Testing Diao, Enmao Banerjee, Taposh Tarokh, Vahid Signal Processing Methodology Score-based statistical models play an important role in modern machine learning, statistics, and signal processing. For hypothesis testing, a score-based hypothesis test is proposed in \cite{wu2022score}. We analyze the performance of this score-based hypothesis testing procedure and derive upper bounds on the probabilities of its Type I and II errors. We prove that the exponents of our error bounds are asymptotically (in the number of samples) tight for the case of simple null and alternative hypotheses. We calculate these error exponents explicitly in specific cases and provide numerical studies for various other scenarios of interest. |
| title | Large Deviation Analysis of Score-based Hypothesis Testing |
| topic | Signal Processing Methodology |
| url | https://arxiv.org/abs/2401.15519 |