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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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TL;DR: In this paper, a continuous process monitoring procedure that does not require 100% inspection is proposed. But the procedure is a composite of two cumulative sum procedures and is not suitable for the case of discrete variates under observation.
Abstract: Beattie (1962) devised a continuous process-monitoring procedure that does not require 100% inspection. The procedure is a composite of two cumulative sum procedures. He considered the case in which the variate under observation is discrete. Here we extend Beattie's procedure to monitor a process when the observed variate is normally distributed. Additionally, we show how this procedure can be used to monitor two-sided variations of the parameter under observation. We use Wald's approximating expressions for the sequential probability ratio test to obtain an approximate operating characteristic (OC) curve for the procedure. We also evaluate the accuracy of the OC-curve expression and use it to develop design guidelines. Two examples demonstrate the simplicity of the design rules.
TL;DR: In this paper, the sequential probability ratio test (SPRT) is implemented for multivariate normal distribution, for independent and autocorrelated data and known covariance matrix and the methodo...
Abstract: In this article Wald’s sequential probability ratio test (SPRT) is implemented for multivariate normal distribution, for independent and autocorrelated data and known covariance matrix. The methodo...
17 Aug 2000
TL;DR: The M-ary sequential probability ratio test MSPRT is used to recognize unknown non-cooperative radar targets and scenarios representing various degrees of azimuth uncertainty are examined in this paper.
Abstract: The M-ary sequential probability ratio test MSPRT is used to recognize unknown non-cooperative radar targets. Radar returns representing the unknown target backscatter coefficients are tested sequentially using MSPRT. At each stage of the recognition process all observations are used in MSPRT, if no identification decision can be made, additional information is requested and MSPRT is implemented again. The goal is either to minimize the number of observations needed to identify an unknown target assuming a certain predetermined error probability, or to minimize the probability of error assuming a predetermined maximum number of observations. The experimental phase of this study involves radar cross-section signatures of four commercial aircraft models recorded in a compact range environment. Scenarios representing various degrees of azimuth uncertainty are examined in this paper. In all cases, it is assumed that the unknown target is corrupted with additive white Gaussian noise.
01 Dec 2015
TL;DR: In order to validate the lifetime of long-life product, the concept of lifetime demonstration testing (LDT) based on degradation is presented and a Bayesian reliability model based on a generalized degradation process is presented.
Abstract: In order to validate the lifetime of long-life product, the concept of lifetime demonstration testing (LDT) based on degradation is presented in this paper. For shortening test time as far as possible, the prior information of similar products is considered. Therefore, a framework of Bayesian lifetime demonstration based on degradation is developed by considering the prior information of products. Firstly, we present a Bayesian reliability model based on a generalized degradation process, and take a linear form for example to infer the posterior cumulative distribution function (CDF). Due to the prior information considered, the Bayesian sequential probability ratio test (SPRT) method is used to demonstrate lifetime of the product at test censored time. And the corresponding decision rules are given under the producer's risk α and consumer's risk β. Finally, for an illustration of the proposed model and method, an example about the self-healing metallized film pulse capacitors is discussed and some interesting and exciting analytical results are presented.1
01 Jan 1997
TL;DR: In this article, the problem of automatic monitoring the weld quality when welding with Gas Metal Arc (GMA) in short circuiting mode is dealt with, where a simple statistical change detection algorithm, the repeated sequential probability ratio test (SPRT), is used.
Abstract: This paper deals with the problem of automatic monitoring the weld quality when welding with Gas Metal Arc (GMA) in short circuiting mode. Experiments with two different types of T-joints are performed in order to provokeoptimal and non-optimal welding conditions. During the experiments, voltage and current are measured from the welding process. A simple statistical change detection algorithm for the weld quality, the repeated Sequential Probability Ratio Test (SPRT), is used. The algorithm can equivalently be viewed as a cumulative sum (CUSUM) - type test. The test statistics is based upon the fluctuations of amplitude in the the weld voltage. It is shown that the fluctuations of the weld voltage amplitude decreases when the welding process is not operating under optimal condition. The results obtained from the experiments indicate that it is possible to detect changes in the weld quality automatically and on-line.