Bounding the maximum likelihood degree
Algebraic Geometry
2015-04-20 v2 Statistics Theory
Statistics Theory
Abstract
Maximum likelihood estimation is a fundamental computational problem in statistics. In this note, we give a bound for the maximum likelihood degree of algebraic statistical models for discrete data. As usual, such models are identified with special very affine varieties. Using earlier work of Franecki and Kapranov, we prove that the maximum likelihood degree is always less or equal to the signed intersection-cohomology Euler characteristic. We construct counterexamples to a bound in terms of the usual Euler characteristic conjectured by Huh and Sturmfels.
Cite
@article{arxiv.1411.3486,
title = {Bounding the maximum likelihood degree},
author = {Nero Budur and Botong Wang},
journal= {arXiv preprint arXiv:1411.3486},
year = {2015}
}
Comments
v2: final version, to appear in Math. Res. Lett