Information and Statistical Efficiency When Quantizing Noisy DC Values

This paper considers estimation of a quantized constant in noise when using uniform and nonuniform quantizers. Estimators based on simple arithmetic averages, on sample statistical moments and on the maximum-likelihood procedure are considered. It provides expressions for the statistical efficiency of the arithmetic mean by comparing its variance to the proper Cramér-Rao lower bound. It is conjectured that the arithmetic mean is optimal among all estimators with an exactly known bias. Conditions under which its statistical performance are improved by the other estimation procedures when the exact bias is not known are found and analyzed. Using simulations and analysis of experimental data, it is shown that both moment-based and maximum-likelihood-based estimators provide better results, when the noise standard deviation is comparable with the quantization step and the noise model of quantization can not be applied.