Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample

TL;DR

This paper investigates maximum likelihood estimation for Brownian Motion Tree Models with a single data sample, proving existence, uniqueness, and providing efficient algorithms, with applications in phylogenetics.

Contribution

It establishes the existence and uniqueness of the one-sample BMTM MLE, and introduces a polynomial time algorithm for its exact computation, advancing statistical methods in phylogenetics.

Findings

MLE exists and is unique almost surely

A polynomial time algorithm for exact MLE computation

The MLE over all BMTM structures coincides with a path graph solution

Abstract

We study the problem of maximum likelihood estimation given one data sample ($n=1$) over Brownian Motion Tree Models (BMTMs), a class of Gaussian models on trees. BMTMs are often used as a null model in phylogenetics, where the one-sample regime is common. Specifically, we show that, almost surely, the one-sample BMTM maximum likelihood estimator (MLE) exists, is unique, and corresponds to a fully observed tree. Moreover, we provide a polynomial time algorithm for its exact computation. We also consider the MLE over all possible BMTM tree structures in the one-sample case and show that it exists almost surely, that it coincides with the MLE over diagonally dominant M-matrices, and that it admits a unique closed-form solution that corresponds to a path graph. Finally, we explore statistical properties of the one-sample BMTM MLE through numerical experiments.