Minimax Quantization for Distributed Maximum Likelihood Estimation
Minimax Quantization for Distributed Maximum Likelihood Estimation
复制标题
分布式最大似然估计的极小极大量化
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
A. Swami
中科院分区：
文献类型：
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作者：
P. Venkitasubramaniam;L. Tong;A. Swami
我们考虑的量化器的分布式估计的确定性参数的设计，当融合中心使用最大似然估计。我们定义了一个新的性能指标，这是最大限度地减少非量化和量化的观察Fisher信息之间的比率。由于估计量是M-L的，因此该准则等价于最小化由于量化而导致的最大渐近相对效率。我们提出了一种算法来获得量化器，优化的度量，并证明其收敛性。通过仿真，我们说明了量化器的性能是接近最好的可能的Fisher信息作为量化比特数的增加。此外，在一定条件下，量化器结构被发现属于一类得分函数量化器，它最大化费舍尔信息为一个给定的值的参数
We consider the design of quantizers for the distributed estimation of a deterministic parameter, when the fusion center uses a maximum-likelihood estimator. We define a new metric of performance, which is to minimize the maximum ratio between the Fisher information of the unquantized and quantized observations. Since the estimator is M-L, the criterion is equivalent to minimizing the maximum asymptotic relative efficiency due to quantization. We propose an algorithm to obtain the quantizer that optimizes the metric and prove its convergence. Through simulations, we illustrate that the quantizer performance is close to the best possible Fisher information as the number of quantization bits increases. Furthermore, under certain conditions, the quantizer structure is found to belong to the class of score-function quantizers, which maximizes Fisher information for a given value of the parameter