Regularized weighted Chebyshev approximations for support estimation

We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the stateof-the-art techniques in the area and outperforms them in practice. In particular, we present both theoretical and computer simulation results that illustrate the utility and performance improvements of our method. The theoretical analysis relies on introducing a new weighted Chebyshev polynomial approximation method, jointly optimizing the bias and variance components of the risk, and combining the weighted minmax polynomial approximation method with discretized semi-infinite programming solvers. Such a setting allows for casting the estimation problem as a linear program (LP) with a small number of variables and constraints that may be solved as efficiently as the original Chebyshev approximation problem. Our technique is tested on synthetic data and textual data (Shakespeare’s plays), and is used to address an important problem in computational biology - estimating the number of bacterial genera in the human gut. On synthetic datasets, for practically relevant sample sizes, we observe significant improvements in the value of the worst-case risk compared to existing methods. The same is true of the text data. For the bioinformatics application, using metagenomic data from the NIH Human Gut and the American Gut Microbiome Projects, we generate a list of frequencies of bacterial taxa that allows us to estimate the number of bacterial genera to ∼ 2300.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Chien, I

Contributors dc:contributor

• Milenkovic, Olgica

• Support estimation
• Chebyshev polynomials

Statement dc:rights

• Copyright I Chien 2019

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/106187

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/106187