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.

Sequential Analysis and Optimal Design

Abstract: Preliminaries on probability Generalities about the conventional theory of design of experiments Optimal sample size Preliminaries on regression Design for linear regression: Elfving's method Maximum-likelihood estimation Locally optimal designs for estimation More design in regression experiments Testing hypotheses Optimal sample size in testing Sequential probability-ratio test Optimality of sequential probability-ratio test Motivation for an approach to sequential design of experiments in testing hypotheses Asymptotic optimality of procedure A in sequential design Extensions and open questions in sequential design The problem of adjacent hypotheses Testing for the sign of a normal mean: no indifference zone Bandit problems Sequential estimation of a normal mean sequential estimation of the common mean of two normal populations.

A sequential procedure for multihypothesis testing

Abstract: The sequential testing of more than two hypotheses has important applications in direct-sequence spread spectrum signal acquisition, multiple-resolution-element radar, and other areas A useful sequential test which we term the MSPRT is studied in this paper The test is shown to be a generalization of the sequential probability ratio test Under Bayesian assumptions, it is argued that the MSPRT approximates the much more complicated optimal test when error probabilities are small and expected stopping times are large Bounds on error probabilities are derived, and asymptotic expressions for the stopping time and error probabilities are given A design procedure is presented for determining the parameters of the MSPRT Two examples involving Gaussian densities are included, and comparisons are made between simulation results and asymptotic expressions Comparisons with Bayesian fixed sample size tests are also made, and it is found that the MSPRT requires two to three times fewer samples on average >

Use of risk-adjusted CUSUM and RSPRT charts for monitoring in medical contexts.

Abstract: In this paper we discuss the use of charts derived from the sequential probability ratio test (SPRT): the cumulative sum (CUSUM) chart, RSPRT (resetting SPRT), and FIR (fast initial response) CUSUM. The theoretical development of the methods is described and some considerations one might address when designing a chart, explored, including the approximation of average run lengths (ARLs), the importance of detecting improvements in a process as well as detecting deterioration and estimation of the process parameter following a signal. Two examples are used to demonstrate the practical issues of quality control in the medical setting, the first a running example and the second a fully worked example at the end of the paper. The first example relates to 30-day mortality for patients of a single cardiac surgeon over the period 1994-1998, the second to patient deaths in the practice of a single GP, Harold Shipman. The charts' performances relative to each other are shown to be sensitive to the definition of the 'out of control' state of the process being monitored. In light of this, it is stressed that a suitable means by which to compare charts is chosen in any specific application.

WaldBoost - learning for time constrained sequential detection

Abstract: In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of sequential decision-making. If the false positive and false negative error rates are given, the optimal strategy in terms of the shortest average time to decision (number of measurements used) is the Wald's sequential probability ratio test (SPRT). We built on the optimal SPRT test and enlarge its capabilities to problems with dependent measurements. We show how to overcome the requirements of SPRT - (i) a priori ordered measurements and (ii) known joint probability density functions. We propose an algorithm with near optimal time and error rate trade-off, called WaldBoost, which integrates the AdaBoost algorithm for measurement selection and ordering and the joint probability density estimation with the optimal SPRT decision strategy. The WaldBoost algorithm is tested on the face detection problem. The results are superior to the state-of-the-art methods in the average evaluation time and comparable in detection rates.

F-8 DFBW sensor failure identification using analytic redundancy

Abstract: In this paper, we outline the structure of a sensor failure detection and identification system designed for the NASA F-8 DFBW aircraft The system is for use in a dual-redundant environment, and it takes maximal advantage of all functional relationships among the sensed variables The identification logic uses the quality sequential probability ratio, which provides a useful on-line measure of confidence in the various forms of analytic redundancy Preliminary simulation results indicate good behavior of the analytic decision statistic, based on the sequential probability ratio test