This paper characterizes the fundamental limits of mean testing under arbitrary truncation, where a portion of the probability mass is hidden. The research identifies a detectability floor created by truncation bias and proposes a second-order test with near-optimal sample complexity. Additionally, it reveals a method to escape this bias barrier under a directional median regularity assumption, improving the bias to linear order and recovering classical statistical rates. AI
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IMPACT Provides theoretical underpinnings for statistical methods that could be applied in machine learning contexts.
RANK_REASON This is a research paper published on arXiv detailing theoretical statistical findings.