Date of Award
Department of Computer Science
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. , which provides a protocol under the assumption that the distribution is Gaussian, as well as from Duchi et al. , 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.
Jin, Matthew, "Probabilistic Error Upper Bounds For Distributed Statistical Estimation" (2020). Dartmouth College Undergraduate Theses. 208.