The Statistical Performance of Collaborative Inference

Gérard Biau; Kevin Bleakley; Benoît Cadre

The statistical analysis of massive and complex data sets will require the development of algorithms that depend on distributed computing and collaborative inference. Inspired by this, we propose a collaborative framework that aims to estimate the unknown mean

$\theta$ of a random variable

$X$ . In the model we present, a certain number of calculation units, distributed across a communication network represented by a graph, participate in the estimation of

$\theta$ by sequentially receiving independent data from

$X$ while exchanging messages via a stochastic matrix

$A$ defined over the graph. We give precise conditions on the matrix

$A$ under which the statistical precision of the individual units is comparable to that of a (gold standard) virtual centralized estimate, even though each unit does not have access to all of the data. We show in particular the fundamental role played by both the non-trivial eigenvalues of

$A$ and the Ramanujan class of expander graphs, which provide remarkable performance for moderate algorithmic cost.

The Statistical Performance of Collaborative Inference

Abstract