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We define a new metric of performance, which is to minimize the maximum ratio between the Fisher Information of the unquantized and quantized observations.
We consider the design of quantizers for the distributed estimation of a de- terministic parameter, when the fusion center uses a Maximum-Likelihood.
Minimax Quantization for Distributed Maximum Likelihood Estimation. Venkitasubramaniam P., Lang Tong, Swami A. Expand. Publication type: Proceedings Article.
The problem of distributed estimation using one-bit quantization in the presence of non-ideal transmission links is revisited and the uniform thresholding ...
We study the problem of quantization for distributed parameter estimation in large scale sensor networks. Assuming a maximum likelihood estimator at the ...
ABSTRACT. We study the problem of quantization for distributed para- meter estimation in large scale sensor networks. Assuming a Maximum Likelihood ...
Abstract—We study the problem of quantization for distrib- uted parameter estimation. We propose the design of score- function quantizers to optimize ...
Abstract. We formulate the notion of minimax estimation under storage or communication constraints, and prove an extension to Pinsker's theorem for non-par.
The Wasserstein metric is an important measure of distance between probability distributions, with many applications in machine learning, statistics, ...
We study the rates of estimation of finite mixing distributions, that is, the parameters of the mixture. We prove that under some regularity and strong ...