On the Optimality of Several Algorithms on Polynomial Regression of Empicial Bayes Poisson Model

The empirical Bayes estimator for the Poisson mixture model in [1], [2] has been an important problem studied for the past 70 years. In this thesis, we investigate extensions of this problem to estimating polynomial functions of the Poisson parameter rather than just the parameter itself. We generalize three different algorithms for estimation, specifically the Robbins estimator from [2], the NPMLE method from [3], and the ERM method from [4]. For each of these algorithms, we prove upper bounds on the minimax regret. We also prove a general lower bound that applies to any estimation algorithm for this setup. In addition to the theoretical bounds, we empirically simulate the performance of all three algorithms in relation to both the number of sample and the degree of the polynomial function we estimate.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Kang, Benjamin

Advisor dc:contributor.advisor

• Polyanskiy, Yury

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156808

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156808