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.

Optimum Partial Sequential Tests for Two-Sided Tests of the Binomial Parameter

Abstract: An optimum partial sequential procedure for testing a null hypothesis concerning the binomial parameter with a two-sided alternative hypothesis is described. Formulas for its operating characteristic and average sample number functions are derived. By approximating an Armitage procedure by a special case of this partial procedure, approximate values can be obtained for his operating characteristic and average sample number functions.

Fuzzy Hypothesis Testing for Cooperative Sequential Spectrum Sensing Under Noise Uncertainty

Abstract: Efficient and reliable spectrum sensing is extremely significant, especially in the presence of noise uncertainty in low SNR environment below which conventional detectors fail to be robust. In this letter, by the development of a sequential probability ratio test for the fuzzy hypothesis testing (FHT), we propose a novel cooperative sequential detector to deal with the effect of noise power uncertainty. In this approach, for every measurement, FHT is computed by each cognitive radio. Subsequently, fusion center sequentially accumulates these fuzzy test statistics and decides about the sensing time. Simulation results are illustrated to show the effectiveness and robustness of the proposed sequential FHT detector. The significant reduction in sample complexity is demonstrated for our scheme in comparison with energy detector, sequential crisp hypothesis testing detector, and fixed sample size FHT detector.

Neural network based data validation algorithm for pressure processes

Abstract: Effective Control of Complex process in power generation industries is a challenge to instrumentation engineers. Most importantly coordination of sensor functioning should be monitored regularly. So, a sensor validation and data reconciliation methods are adopted for monitoring the data and faulty data are replaced. In this paper neural network based data validation method is developed and tested for pressure sensor. For neural networks, back propagation algorithm is applied for obtaining an estimated value and then a fault detection method called SPRT (Sequential probability ratio test) is applied to identify the trueness of the sensor.

Sequential analysis applied to testing the mean of an inverse gaussian distribution with known coefficient of variation

Abstract: Given an inverse Gaussian distribution I(.μ,a2μ) with known coefficient of variation a, the hypothesis HO: .μ μo is tested against H1: μ μ1 using the sequential probability ratio test. The maximum of the expected sample number is shown to occur when μ is approximately equal to the geometric mean of μoand μ1 and it is shown that this maximum value depends on .μo and μ1 only through their ratio. It is observed that the test can be used to discriminate between two one-sided hypotheses.

Acquisition of PN Sequences for DS/SS Systems using a truncated sequential probability ratio test

Abstract: An acquisition scheme for a direct sequence spread spectrum signal using the truncated sequential probability ratio test (TSPRT) was studied. Coherent demodulation is assumed. For testing the synchronization, a sliding-correlator linear detector was employed. Since the partial correlation of PN sequences is difficult to characterize by a simple model, the worst case was considered. Linearized bounds of the partial correlation were used for designing the TSPRT. The design parameters of the TSPRT are chosen so that the average sample number (ASN) is approximately minimized while the maximum ASN is always smaller than that for the fixed dwell scheme with similar false alarm and miss probabilities. Some simulation results were obtained and they agree well with analytic results.