Universal Quantile Estimation with Feedback in the Communication-Constrained Setting
Ram Rajagopal, Martin J. Wainwright

TL;DR
This paper investigates decentralized quantile estimation under communication constraints, proposing protocols that match or approach the centralized optimal mean-squared error decay rates, with analysis and simulations demonstrating their effectiveness.
Contribution
It introduces new decentralized protocols with limited feedback that achieve near-optimal or optimal asymptotic mean-squared error decay rates in quantile estimation.
Findings
A protocol with bits of feedback matches centralized asymptotic MSE.
A single-bit feedback protocol achieves MSE decay with appropriate step sizes.
Simulations confirm the theoretical tradeoffs and performance of the proposed protocols.
Abstract
We consider the following problem of decentralized statistical inference: given i.i.d. samples from an unknown distribution, estimate an arbitrary quantile subject to limits on the number of bits exchanged. We analyze a standard fusion-based architecture, in which each of sensors transmits a single bit to the fusion center, which in turn is permitted to send some number bits of feedback. Supposing that each of sensors receives observations, the optimal centralized protocol yields mean-squared error decaying as . We develop and analyze the performance of various decentralized protocols in comparison to this centralized gold-standard. First, we describe a decentralized protocol based on bits of feedback that is strongly consistent, and achieves the same asymptotic MSE as the centralized optimum. Second, we describe and analyze a…
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Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms · Target Tracking and Data Fusion in Sensor Networks · Gaussian Processes and Bayesian Inference
