Sequential probability ratio test

About: Sequential probability ratio test is a research topic. Over the lifetime, 1248 publications have been published within this topic receiving 22355 citations.

Cooperative detection of primary user emulation attacks based on channel-tap power in mobile cognitive radio networks

Abstract: This work proposes a new method for identifying a primary user emulation attack PUEA at low signal-to-noise ratios SNRs in mobile cognitive radio CR networks. Channel-tap power is utilised as a radio-frequency RF fingerprint to directly detect users via physical PHY layer. To improve the detection performance in fading channels, the cooperative detection schemes using the fixed sample size test FSST and the sequential probability ratio test SPRT are devised. From simulation results, for a mobile CR speed of 70 km/h, SNR = -5 dB and false alarm probability of 0.03, the FSST using 10 cooperative nodes can achieve the detection probability of 0.99, which is increased by 1.94 times that of the single Neyman-Pearson detector. When the SPRT is compared to the FSST, the SPRT only needs less than half of the detection time of the FSST to provide the same detection performance for all SNRs considered.

Comment on "Optimality of the sequential probability ratio test for nonstationary observations" by Y. Liu and S.D. Blostein

Abstract: For original paper see IEEE Trans. Inform. Theory, vol.38, p.177-82 (1992). Liu and Blostein (1992) consider sequential tests with varying stopping bounds (NSPRT) for testing simple hypotheses on independent but nonstationary observations. They state a considerable generalization of the famous Wald-Wolfowitz theorem [1948] on the (simultaneous) minimization of the expected sample sizes for given error probabilities (Liu and Blostein, Theorem 27). Unfortunately, this statement fails to be true: 1) a simple counterexample may be constructed by choosing densities f/sub 01//spl ne/f/sub 1,1/ such that 0 >

Extended SPRT for structural change detection of time series based on a multiple regression model

Abstract: It is important to detect a structural change in a time series quickly as a trigger to remodeling the forecasting model. The well-known Chow test has been used as the standard method for detecting change, especially in economics. However, we have proposed the application of the sequential probability ratio test (SPRT) for detecting the change in single-regression modeled time-series data. In this article, we show experimental results using SPRT and the Chow test when applied to time-series data that are based on multiple regression models. We also clarify the effectiveness of SPRT compared with the Chow test in its ability to detect change early and correctly, and its computational complexity. Moreover, we extend the definition of the point at which structural change is detected with the SPRT method, and show an improvement in the accuracy of change detection.