Reconstruction on Trees: Exponential Moment Bounds for Linear Estimators

TL;DR

This paper derives exponential moment bounds for linear estimators used in reconstructing the root state of a Markov chain on an infinite b-ary tree, with implications for evolutionary tree inference.

Contribution

It provides new bounds on the moment-generating functions of the estimator in the Kesten-Stigum phase, extending understanding of its probabilistic behavior.

Findings

Bounds on the moment-generating functions of the estimator and its square.

Results applicable to inference in evolutionary trees.

Enhanced understanding of estimator variance in the reconstruction phase.

Abstract

Consider a Markov chain $(\xi_v)_{v \in V} \in [k]^V$ on the infinite $b$-ary tree $T = (V,E)$ with irreducible edge transition matrix $M$, where $b \geq 2$, $k \geq 2$ and $[k] = \{1,...,k\}$. We denote by $L_n$ the level-$n$ vertices of $T$. Assume $M$ has a real second-largest (in absolute value) eigenvalue $\lambda$ with corresponding real eigenvector $\nu \neq 0$. Letting $\sigma_v = \nu_{\xi_v}$, we consider the following root-state estimator, which was introduced by Mossel and Peres (2003) in the context of the "recontruction problem" on trees: \begin{equation*} S_n = (b\lambda)^{-n} \sum_{x\in L_n} \sigma_x. \end{equation*} As noted by Mossel and Peres, when $b\lambda^2 > 1$ (the so-called Kesten-Stigum reconstruction phase) the quantity $S_n$ has uniformly bounded variance. Here, we give bounds on the moment-generating functions of $S_n$ and $S_n^2$ when $b\lambda^2 > 1$. Our results have implications for the inference of evolutionary trees.