Quantization For Distributed Estimation in Large Scale Sensor Networks

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Learning Distributions from their Samples under Communication Constraints (2019, methodology): “There has been some previous work in analyzing Fisher information from a quantized scalar random variable such as [7], [8], [9], [10].”
A Geometric Characterization of Fisher Information from Quantized Samples with Applications to Distributed Statistical Estimation (2018, methodology): “There has been some previous work in analyzing Fisher information from a quantized scalar random variable such as [5],[6],[7],[8].”
Distributed Kalman filtering based on consensus strategies (2008, unknown): “well-known data fusion formulas; [14], [31], [32], [ 40 ], [39] address quantization issues and”
Minimax Quantization for Distributed Maximum Likelihood Estimation (2006, background): “In an earlier work[7], we had shown the optimality of Score-Function Quantizers in maximizing Fisher Information for a particular θ. Data compression for distributed estimation has also been considered from an information theoretic perspective [8], where nodes encode long sequences of observations.”