期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2004
卷号：66
期号：02
出版社：Indian Statistical Institute
摘要：A symmetric version of the Neyman-Pearson test is developed for discriminating between sets of hypotheses and is extended to encompass a new formulation of the problem of parameter estimation based on finite data sets. Such problems can arise in distributed sensing and localization problems in sensor networks, where sensor data must be compressed to account for communication constraints. In this setting it is natural to focus on methods that balance coarse resolution of the estimates for achieving higher reliability.
