Vol. 11, No. 2, 2020

Download this article
Download this article For screen
For printing
Recent Issues
Volume 11, Issue 2
Volume 11, Issue 1
The Journal
About the Journal
Editorial Board
Submission Guidelines
Submission Form
Policies for Authors
Ethics Statement
ISSN (electronic): 2693-3004
ISSN (print): 2693-2997
To Appear
Other MSP Journals
Maximum likelihood degree of the two-dimensional linear Gaussian covariance model

Jane Ivy Coons, Orlando Marigliano and Michael Ruddy

Vol. 11 (2020), No. 2, 107–123

In algebraic statistics, the maximum likelihood degree of a statistical model is the number of complex critical points of its log-likelihood function. A priori knowledge of this number is useful for applying techniques of numerical algebraic geometry to the maximum likelihood estimation problem. We compute the maximum likelihood degree of a generic two-dimensional subspace of the space of n × n Gaussian covariance matrices. We use the intersection theory of plane curves to show that this number is 2n 3.

algebraic geometry, algebraic statistics, linear Gaussian covariance models, intersection theory, plane curves, maximum likelihood estimation, maximum likelihood degree
Mathematical Subject Classification 2010
Primary: 13P25, 14C17, 14H50, 62H12
Received: 10 September 2019
Revised: 20 May 2020
Accepted: 8 June 2020
Published: 28 December 2020
Jane Ivy Coons
Department of Mathematics
North Carolina State University
Raleigh, NC
United States
Orlando Marigliano
Max-Planck-Institute for Mathematics in the Sciences
Michael Ruddy
Max-Planck-Institute for Mathematics in the Sciences