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20182024
most citedSelf-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models

17 citations · 43 across the 11 of their papers we have counts for

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math.ST20216 cited

Sharp regret bounds for empirical Bayes and compound decision problems

Yury Polyanskiy, Yihong Wu

We consider the classical problems of estimating the mean of an -dimensional normally (with identity covariance matrix) or Poisson distributed vector under the squared loss. In…

math.ST202017 cited

Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models

Yury Polyanskiy, Yihong Wu

Introduced by Kiefer and Wolfowitz \cite{KW56}, the nonparametric maximum likelihood estimator (NPMLE) is a widely used methodology for learning mixture odels and empirical Bayes e…

math.ST20201 cited

Note on approximating the Laplace transform of a Gaussian on a complex disk

Yury Polyanskiy, Yihong Wu

In this short note we study how well a Gaussian distribution can be approximated by distributions supported on . Perhaps, the natural conjecture is that for large the a…

math.ST20201 cited

Extrapolating the profile of a finite population

Soham Jana, Yury Polyanskiy, Yihong Wu

We study a prototypical problem in empirical Bayes. Namely, consider a population consisting of individuals each belonging to one of types (some types can be empty). Withou…

math.ST2019

Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation

Ziv Goldfeld, Kristjan Greenewald, Yury Polyanskiy +1

This paper studies convergence of empirical measures smoothed by a Gaussian kernel. Specifically, consider approximating , for $\mathcal{N}_σ\triangleq\mathcal{…

math.ST2019

Dualizing Le Cam's method for functional estimation, with applications to estimating the unseens

Yury Polyanskiy, Yihong Wu

Le Cam's method (or the two-point method) is a commonly used tool for obtaining statistical lower bound and especially popular for functional estimation problems. This work aims to…