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.

Primary User Traffic Classification in Dynamic Spectrum Access Networks

Abstract: This paper focuses on analytical studies of the primary user (PU) traffic classification problem. Observing that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.

Failure Detection Using a Fuzzy Neural Network with an Automatic Input Selection Algorithm

Abstract: It is well known that the performance of fuzzy neural networks applied to sensor monitoring strongly depends on the selection of inputs. In their applications to sensor monitoring, there are usually a large number of input variables related to a relevant output. As the number of input variables increases, the required training time of a fuzzy neural network increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and moreover, to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this chapter, an automatic input selection routine was described which combines the principal component analysis, correlation analysis, and genetic algorithm to select important input features. Also, whether the sensors fail or not is determined by applying the sequential probability ratio test to the residuals between the estimated signals and the measured signals. The described sensor failure detection method was verified through applications to the steam generator water level, the steam generator steam flowrate, the pressurizer water level, and the pressurizer pressure sensors in pressurized water reactors.

A generalized sequential sign detector for binary hypothesis testing

Abstract: It is known that for fixed error probabilities sequential signal detection based on the sequential probability ratio test (SPRT) is optimum in terms of the average number of signal samples for detection. But, often suboptimal detectors like the sequential sign detector are preferred over the optimal SPRT. When the additive noise statistic is independent and identically distributed (i.i.d.), the sign detector is preferred for its simplicity and nonparametric properties. However, in many practical applications such as the usage of high speed sampling devices the noise is correlated. A generalized sequential sign detector for detecting binary signals in stationary, first-order Markov dependent noise is studied. Under the i.i.d. assumptions, this reduces to the usual sequential sign detector. The optimal decision thresholds and the average sample number for the test to terminate are derived. Numerical results are given to show that the proposed detector exploits the correlation in the noise and hence results in quicker detection. The method can also be extended to Mth order Markov dependence by converting it to a first-order dependence in an extended state space.

The Effect of Item Parameter Estimation Error on Decisions Made Using the Sequential Probability Ratio Test

Abstract: : A series of computer simulations were performed in order to observe the effects of item response theory(IRT)item parameter estimation error on decisions made using an IRT-based sequential probability ratio test. Specifically, the effects of such error on misclassification rates and the average number of items required for either a mastery(pass)or nonmastery(fail) decision were observed under varied SPRT conditions. These conditions include the a priori or nominal type I(alpha)and typeII(beta)error rates, the simple hypotheses tested by the SPRT procedure, and the composition of the item pool(specifically the a, b, and c parameters which characterized the items according to a three-parameter logistic model)used to administer the SPRT. The results of these simulations showed that these SPRT decisions are not greatly affected by this particular level of error in parameter estimates modeled in this study. Misclassification error rates were slightly greater when estimation error in the item parameters was present, but such differences appear to be negligible. Keywords: Adaptive testing.

Sequential Test for Arbitrary Ratio of Mean Times Between Failures

Abstract: This paper describes a planning methodology and tools for a truncated SPRT (sequential probability ratio test) for checking the means ratio of the times between failures (assumed to be exponentially distributed) of two items The problem is considered for the situation in which the ratio may differ from unity, whereby the results are applicable for any specified ratio, or wherever multiple copies of each item are tested simultaneously (group test) The authors present a methodology for optimal test choice and dependences for determining the acceptance/rejection boundaries of such a test with given characteristics Planning and implementation of a group test are illustrated in an example, including substantiation of the choice of the number of new-item copies