Title

Probabilistic Error Upper Bounds For Distributed Statistical Estimation

Author

Matthew Jin, Dartmouth College

Date of Award

7-31-2020

Document Type

Thesis (Undergraduate)

Department or Program

Department of Computer Science

First Advisor

Amit Chakrabarti

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.

Comments

Original Completion Date: June 2017

Listed in the Dartmouth College Computer Science Technical Report Series as TR2020-903.

Recommended Citation

Jin, Matthew, "Probabilistic Error Upper Bounds For Distributed Statistical Estimation" (2020). Dartmouth College Undergraduate Theses. 208.
https://digitalcommons.dartmouth.edu/senior_theses/208