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Statisticians develop new methods for estimating set sizes from samples

A new paper introduces refined methods for estimating the size of a set using cascading exclusion, interpolating between existing techniques. The research develops a general non-asymptotic theory applicable to various problems, including estimating the volume of convex sets and the unseen species problem. It also addresses regression-style predictors, providing non-parametric finite sample error bounds for all cases. AI

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IMPACT Provides theoretical underpinnings for data analysis techniques potentially applicable in AI model evaluation and data sampling.

RANK_REASON Academic paper on statistical estimation techniques.

Read on arXiv stat.ML →

COVERAGE [1]

  1. arXiv stat.ML TIER_1 · Sourav Chatterjee, Persi Diaconis, Susan Holmes ·

    Estimating the size of a set using cascading exclusion

    arXiv:2508.05901v4 Announce Type: replace-cross Abstract: Let $S$ be a finite set, and $X_1,\ldots,X_n$ an i.i.d. uniform sample from $S$. To estimate the size $|S|$, without further structure, one can wait for repeats and use the birthday problem. This requires a sample size of …