Papers › Chebyshev polynomials, moment matching, and optimal estimation of the unseen

Chebyshev polynomials, moment matching, and optimal estimation of the unseen

6 Apr 2015arXiv:1504.01227links table onlyarchive 2025-07-28

Yihong Wu, Pengkun Yang

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We consider the problem of estimating the support size of a discrete distribution whose minimum non-zero mass is at least 1/k. Under the independent sampling model, we show that the sample complexity, i.e., the minimal sample size to achieve an additive error of ϵk with probability at least 0.1 is within universal constant factors of k/logklog²1/ϵ, which improves the state-of-the-art result of k/(ϵ² logk) in \cite{VV13}. Similar characterization of the minimax risk is also obtained. Our procedure is a linear estimator based on the Chebyshev polynomial and its approximation-theoretic properties, which can be evaluated in O(n+log² k) time and attains the sample complexity within a factor of six asymptotically. The superiority of the proposed estimator in terms of accuracy, computational efficiency and scalability is demonstrated in a variety of synthetic and real datasets.

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Albuso0/support mentioned on GitHub report

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2ran · our draft was wrong
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get_sample_size Albuso0/support/support.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · 3c21a2aea30fbfce · report
hist_to_fin Albuso0/support/support.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · 316f4171d5206404 · report
sample_to_fin Albuso0/support/support.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · 63fc527bb9e69717 · report

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