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.

On a wald's equation and average sample number in a sequential signal detection

Abstract: It is shown that in detecting sequentially a deterministic signal 0(0 in white noise 72(0 a similar identity (iii) in theorem 2.1, to the Wald's holds concerning a stopping time r determined by making use of a likelihood ratio. It is also shown that r has finite moments of any order under quite weak conditions over the signal. The exact A. S. N. E{y} in a constant signal case has been obtained and given by (2, 8). It is also considered a detection problem of a constant signal OW a in a coloured noise based on a sub-optimal statistic which become optimal when the noise were white. Similar properties of a stopping time r to those in the white noise case have been obtained in theorem 3.1.

Planning Accelerated Sequential Tests for a Step-Stress Model

Abstract: Conventional reliability demonstration test based on statistical method is widely used in industry as it is simple and convenient to apply. But for the products with high reliability and long life, this test method fails to satisfy the demand for short cycle and low cost, and is liable to cause the phenomenon of over-test and short-test. This paper gives a step-stress accelerated sequential probability ratio test (SPRT) plan assuming the Weibull distribution, making it faster to make decision of accept or reject. By raising the levels of stresses applied to the test product in a specified discrete sequence, the test time can be cut down remarkably. Based on the SPRT method of normal stress level and the linearly cumulative exposure model, the step-stress accelerated sequential test methods and the decision method are discussed. An example is given to illustrate specific application and the superior performance of the proposed method over traditional methods.

Efficient Detection of SPAM messages and SPAM zombies in the Internet using Naïve-Bayesian and Sequential Probability Ratio Test (SPRT)

Abstract: The Internet is a global system of interconnected computer networks that provides the communication to serve billions of users worldwide. Compromised machines in the internet allows the attackers to launch various security attacks such as DDoS, spamming, and identity theft. Compromised machines are the one of the major security threat on the internet. In this paper we address this issue by using Naïve-Bayesian and SPRT to automatically identify compromised machines in a network. Spamming allows the attackers to recruit the large number of compromised machines to generate the SPAM messages by hiding the identity, these compromised machines commonly known as spam zombies. We used Naïve-Bayesian and manual methods to detect the SPAM messages and used SPRT technique to identify the spam zombies from the SPAM messages. We proved that the Naïve-Bayesian approach minimizes the error rate, false positives and false negatives compared to the manual approach in the process of detecting SPAM message. Our evaluation studies based on one day email trace collected in our organization network that shows Naïve-Bayesian and SPRT are the effective and efficient systems in automatically detecting SPAM messages and compromised machines in a network.