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- W1966650560 abstract "We study the problem of quantization for distributed parameter estimation in large scale sensor networks. Assuming a Maximum Likelihood estimator at the fusion center, we show that the Fisher Information is maximized by a score-function quantizer. This provides a tight bound on best possible MSE for any unbiased estimator. Furthermore, we show that for a general convex metric, the optimal quantizer belongs to the class of score function quantizers. We also discuss a few practical applications of our results in optimizing estimation performance in distributed and temporal estimation problems." @default.
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- W1966650560 date "2005-01-01" @default.
- W1966650560 modified "2023-10-03" @default.
- W1966650560 title "Quantization For Distributed Estimation in Large Scale Sensor Networks" @default.
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- W1966650560 doi "https://doi.org/10.1109/icisip.2005.1619423" @default.
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