This paper explores the theoretical underpinnings of best-arm identification in statistics, specifically addressing the use of union bounds and their relationship to familywise error rate (FWER) control. It clarifies how multiplicity adjustments, such as Bonferroni correction, arise in this context, even when the best arm is unique. The authors demonstrate an equivalence between two common hypothesis orientations, showing how multiplicity issues manifest differently but remain present in both. AI
IMPACT This research clarifies statistical methods relevant to machine learning algorithms that involve selecting the best option from multiple choices.
RANK_REASON The item is an academic paper published on arXiv discussing statistical methodology. [lever_c_demoted from research: ic=1 ai=0.7]
- alphaXiv
- arXiv
- Best arm identification
- Bonferroni
- Boole's inequality
- CatalyzeX
- DagsHub
- FWER
- Gotit.pub
- Hugging Face
- Influence Flower
- ScienceCast
- Strong FWER Control
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
