The Johnson-Neyman technique is a statistical tool used most frequently in educational and psychological applications. This paper starts by briefly reviewing the JohnsonNeyman technique and suggesting when it should and should not be used; then several different modifications and extensions of the Johnson-Neyman technique, all of them conceptually simple, are proposed. The close relation betwee...

In this paper, the design of optimal schemes for detecting deterministic narrowband signals with unknown parameters in correlated interference modelled by the recently developed GBK distribution is considered. Theoretical derivations of an optimal detector, in the Neyman-Pearson sense, are given for the case where the signal amplitude and phase are unknown. The performance of the detector is th...

We have developed biometric fusion technique based on stochastic theory. The suggested method is a robust adaptation of the Neyman-Pearson technique to the specifics of biometrics. The method makes possible to achieve almost optimal performance as measured by the ROC curve.

In hypotheses testing, such as other statistical problems, we may confront imprecise concepts. One case is a situation in which both hypotheses and observations are imprecise. In this paper, we redefine some concepts about fuzzy hypotheses testing, and then we give the Neyman-Pearson lemma for fuzzy hypotheses testing with fuzzy observations. Finally, we give some applied examples. Mathematics ...

The discrete time detection of a known constant signal in additive white stationary Laplace noise is considered. The receiver operating characteristics of the Neyman-Pearson optimal detector are presented and compared with those of the linear detector. Also, some results obtained using a Gaussian approximation to the distribution of the test statistic are presented.

We study asymptotic performance of distributed detection in large scale connected sensor networks. Contrasting to canonical parallel networks where a single node has access to local decisions from all other nodes, each node can only exchange information with its direct neighbors in the present setting. We establish that, with each node employing an identical one-bit quantizer for local informat...

We study a hypothesis testing problem with privacy constraint over noisy channel and derive the performance of optimal tests under Neyman-Pearson criterion. The fundamental limit interest is privacy-utility tradeoff (PUT) between exponent type-II error probability leakage information source subject to constant on type-I probability. provide an exact characterization asymptotic PUT for any non-v...

We study asymptotic performance of distributed detection in large scale connected sensor networks. Contrasting to canonical parallel networks where a single node has access to local decisions from all other nodes, each node can only exchange information with its direct neighbors in the present setting. We establish that, with each node employing an identical one-bit quantizer for local informat...