Non-parametric distributed quantization-estimation using wireless 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].”
Minimax Quantization for Distributed Maximum Likelihood Estimation (2006, methodology): “When the observation is a deterministic parameter in additive noise, Ribiero and Giannakis[6] showed that the Fisher Information is maximized when the threshold of a 1−bit quantizer is equal to the parameter θ.”
Bandwidth-constrained distributed estimation for wireless sensor networks-part II: unknown probability density function (2006, result): “A portion of the results in this paper appeared in [21] and [22].”
Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information (2019, methodology): “This framework was first introduced in Barnes et al. (2018, 2019), and there has been some previous work in analyzing Fisher information from a quantized scalar random variable such as Venkitasubramaniam et al. (2006, 2005); Ribeiro and Giannakis (2005); Lam and Reibman (1993).”
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].”