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Probabilistic Error Upper Bounds For Distributed Statistical Estimation
Matthew G. Jin
Dartmouth TR2020-903

Abstract: The size of modern datasets has spurred interest in distributed statistical estimation. We consider a scenario in which randomly drawn data is spread across a set of machines, and the task is to provide an estimate for the location parameter from which the data was drawn. We provide a one-shot protocol for computing this estimate which generalizes results from Braverman et al. [2], which provides a protocol under the assumption that the distribution is Gaussian, as well as from Duchi et al. [4], which assumes that the distribution is supported on the compact set [−1,1]. Like that of Braverman et al., our protocol is optimal in the case that the distribution is Gaussian.

Note: Senior Honors Thesis. Advisor: Amit Chakrabarti. Original Completion Date: June 2017

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   Matthew G. Jin, "Probabilistic Error Upper Bounds For Distributed Statistical Estimation." Dartmouth Computer Science Technical Report TR2020-903, July 2020.

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