A General Framework for Symmetric Property Estimation
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Publication:6335961
arXiv2003.00844MaRDI QIDQ6335961
Author name not available (Why is that?)
Publication date: 2 March 2020
Abstract: In this paper we provide a general framework for estimating symmetric properties of distributions from i.i.d. samples. For a broad class of symmetric properties we identify the easy region where empirical estimation works and the difficult region where more complex estimators are required. We show that by approximately computing the profile maximum likelihood (PML) distribution cite{ADOS16} in this difficult region we obtain a symmetric property estimation framework that is sample complexity optimal for many properties in a broader parameter regime than previous universal estimation approaches based on PML. The resulting algorithms based on these pseudo PML distributions are also more practical.
Has companion code repository: https://github.com/shiragur/CodeForPseudoPML
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