Topic
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.
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21 Dec 2015
TL;DR: In this paper, an improvement of IEC-61123 in the field of sequential probability ratio test (SPRT) is proposed, which is a significant improvement over the existing one.
Abstract: An improvement of IEC-61123 in the field of sequential probability ratio test (SPRT) is proposed. The current standard does not provide a proper solution for modern industry's needs, and the test plans are not up-to-date with the knowledge in the area of sequential tests. The advantages of the proposed version are reflected by the efficacy and accuracy of the tests, the wider range of the ready to use test's parameters, and available data regarding the test's characteristics. The proposed version is a significant improvement over the existing one. The changes will extend the use of SPRT and this standard.
2 citations
01 Jan 2009
TL;DR: In this paper, a simulation study of the sequential probability ratio test (SPRT) was performed and the results showed that the larger the difference between p0 and p1, the quicker one could reject the null hypothesis.
Abstract: Unlike the classical hypothesis test, the method of sequential probability ratio test (SPRT) checks the outcome sequentially. Based on the outcome, the test can have one of three test results: reject the null hypothesis, accept the null hypothesis, or continue the SPRT procedure. Consequently, the SPRT method may reduce the required sample size for performing a test as compared to a classical hypothesis test. A simulation study of SPRT was performed. A variety of comparisons between p0 and p1 were simulated with p0 =0.001 and p1 between 0.0011 and 0.0023. The target rates of the 12 time points for each group were from 0 out of 10 to 30 out of 10 with a constant increment of 2/10. The results showed that the larger the difference between p0 and p1, the quicker one could reject the null hypothesis. Therefore, we can expect a quicker response when p0/p1 has larger differences. Key word: SPRT, Sequential Probability Ratio Test, Sequential Test, Simulation Study.
2 citations
11 Sep 2003
TL;DR: A sequential probability ratio test (SPRT) on ground penetrating radar (GPR) data is used to detect buried antipersonnel land mines and to reject clutter objects and the performance of the fixed-depth and variable-depth detection algorithms versus noise and SPRT threshold value is evaluated.
Abstract: In this paper we use a sequential probability ratio test (SPRT) on ground penetrating radar (GPR) data to detect buried antipersonnel land mines and to reject clutter objects. Detection is performed for both fixed-depth and variable-depth cases. We use high-dimensional analysis of variance (HANOVA) to window the GPR data before SPRT analysis. Our algorithm uses a library of mine and clutter objects and performs a series of SPRTs using the object library for each unknown image. We also evaluate the performance of our fixed-depth and variable-depth detection algorithms versus noise and SPRT threshold value.
2 citations
TL;DR: In this article, the authors discuss some possible sequential procedures based on sample range and mean range for the ratio of two variances, o- and o2, of two distinct normal populations with known or unknown means.
Abstract: The idea of using range in place of sample standard deviation in sequential testing of hypotheses is not new. Cox (1949) proposed a sequential test based on sample range for the simple hypothesis of testing the standard deviation of a normal population. He and later Rushton (1952) constructed a sequential test based on the ratio of two mean ranges for testing the ratio of standard deviations of two normal populations. Quite recently Gilchrist (1961) proposed a sequential range test for the mean of a normal population. The purpose of the present note is to discuss some possible sequential procedures based on sample range and mean range for the ratio of two variances, o- and o2, of two distinct normal populations with known or unknown means. More precisely, our problem is to discriminate between the pair of composite hypotheses
2 citations
14 Oct 1996
TL;DR: Simulations have shown that this learning detector can operate as optimum as SPRT while using much less statistical knowledge than SPRT.
Abstract: This paper presents a neural network method for sequential detection. A theorem is stated that there exists a reinforcement learning algorithm which can approach the performance of the optimal sequential probability ratio test (SPRT) in the minimum mean squared-error. Then a suitable network architecture and learning algorithm are developed to implement the reinforcement learning. Simulations have shown that this learning detector can operate as optimum as SPRT while using much less statistical knowledge than SPRT.
2 citations