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 thi...

Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric...

Professor Rolfe, having denied that he and Freshwater made the mistake we accused them of (Paley et al. 2007), promptly makes it again (Rolfe 2008). Towards the end of his JAN FORUM piece, he says: ‘evidence-based treatments are predicated on the failure to find evidence that confirms the null hypothesis’ (p. 268). In other words, he thinks that evidence-based treatments are justified by a lack...