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.

A note on testing the mems of normal distributions with known coefficient of variations

Abstract: We consider two problems in sequential hypothesis testing. The first one deals with testing the mean of a normal distribution with known coefficient of variation. The standard sequential probability ratio test (SPRl) and the fixed-sample test are discussed and compared. The second problem, deals with a version of the Behrens-Fisher problem, for comparing the means of two normal-distributions with unknown variances,, Sequential and fixed-sample tests are developed when either (i) the coefficient of variations are known or (ii) the two distributions have the same unknown coefficient of variation. The fixed-sample test is compared with the" proposed sequential procedure.

A Context-Dependent Sequential Decision for Speaker Verification

Abstract: This paper presents an improved method of speaker verification using the sequential probability ratio test (SPRT), which can treat the correlation between successive feature vectors. The hidden Markov model with the mean field approximation enables us to consider the correlation in the SPRT, i.e., using the mean field of previous state, probability computation can be carried out as if input samples were independent each other. key words: speaker verification, sequential probability ratio test, sequential decision, mean field approximation, HMM

Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test

Abstract: To date, Wald sequential probability ratio test (WSPRT) has been widely applied to track management of multiple hypothesis tracking (MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT (SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.

Anomaly Detection of Polymer Resettable Circuit Protection Devices

Abstract: As circuit protection devices, failure or abnormal behavior of polymer positive-temperature-coefficient resettable devices can cause damage to circuits. It is necessary to detect anomalies in the resettable circuit protection devices to provide early warning of failure and avoid damage to a circuit. In this paper, a novel anomaly detection method, the cross-validation-based sequential probability ratio test, is developed and applied to the failure precursor parameters of the resettable circuit protection devices to conduct anomaly detection. The cross-validation-based sequential probability ratio test integrates the advantages of both the sequential probability ratio test for in situ anomaly detection and the cross-validation technique for model parameter selection to reduce the probability of false and missed alarms in anomaly detection. The cross-validation-based sequential probability ratio test solves the model parameter selection difficulty of the traditional sequential probability ratio test and improves its performance in anomaly detection.

A generalized Shiryayev sequential probability ratio test for change detection and isolation

Abstract: We derive an online multiple hypothesis Shiryayev sequential probability test (SSPRT) by adopting a dynamic programming approach. It is shown that for a certain criterion of optimality, this extended Shiryayev SPRT detects and isolates the occurrence of a failure in the conditionally independent measurement sequence in minimum time, unlike the Wald SPRT, which detects the presence/absence of a failure in the entire measurement sequence. We consider the measurement cost, the cost of a false alarm and the cost of a miss-alarm in our dynamic programming analysis. The algorithm is shown to be optimal even in the asymptotic sense and the theoretical results have been extended to the detection and identification of changes with unknown parameters, Finally, the performance of the algorithm is evaluated by using a few examples. In particular, we implement the algorithm in a fault detection and identification scheme for advanced vehicle control systems.