We study schemes and lower bounds for distributed minimax statistical estimation over a
Gaussian multiple-access channel (MAC) under squared error loss. Our framework …

We study lower bounds for minimax statistical estimation over a Gaussian multiple-access
channel (MAC) under squared error loss, using techniques from both statistical estimation …

We study the problem of communicating sensor readings over a Gaussian multiaccess
(MAC) channel. We focus on the scenario that each sensor observes a single random …

We consider distributed estimation of a Gaussian source in a heterogenous bandwidth
constrained sensor network, where the source is corrupted by independent multiplicative …

We study distributed estimation of a Gaussian mean under communication constraints in a
decision theoretical framework. Minimax rates of convergence, which characterize the …

We consider the Gaussian multiple-access channel with two critical departures from the
classical asymptotics: a) number of users proportional to block-length and b) each user …

We consider the problem of distributed estimation, where local processors observe
independent samples conditioned on a common random parameter of interest, map the …

We study the problem of communicating sensor readings over a Gaussian multiaccess
channel. We focus on the scenario that each sensor observes a single random variable and …

Consider the Fisher information for estimating a vector θ∈ ℝ d from the quantized version of
a statistical sample X~ f (x| θ). Let M be a k-bit quantization of X. We provide a geometric …

We derive lower bounds on the Bayes risk in decentralized estimation, where the estimator
does not have direct access to the random samples generated conditionally on the random …