  This paper considers the distributed computation of confidence regions
tethered to multidimensional parameter estimation under linear measurement
models. In particular, the considered confidence regions are non-asymptotic,
this meaning that the number of required measurements is finite. Distributed
solutions for the computation of non-asymptotic confidence regions are
proposed, suited to wireless sensor networks scenarios. Their performances are
compared in terms of required traffic load, both analytically and numerically.
The evidence emerging from the conducted investigations is that the best
solution for information exchange depends on whether the network topology is
structured or unstructured. The effect on the computation of confidence regions
of information diffusion truncation is also examined. In particular, it is
proven that consistent confidence regions can be computed even when an
incomplete set of measurements is available.
