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.

Controlling decision errors with minimal costs: The sequential probability ratio t test.

Abstract: For several years, the public debate in psychological science has been dominated by what is referred to as the reproducibility crisis. This crisis has, inter alia, drawn attention to the need for proper control of statistical decision errors in testing psychological hypotheses. However, conventional methods of error probability control often require fairly large samples. Sequential statistical tests provide an attractive alternative: They can be applied repeatedly during the sampling process and terminate whenever there is sufficient evidence in the data for one of the hypotheses of interest. Thus, sequential tests may substantially reduce the required sample size without compromising predefined error probabilities. Herein, we discuss the most efficient sequential design, the sequential probability ratio test (SPRT), and show how it is easily implemented for a 2-sample t test using standard statistical software. We demonstrate, by means of simulations, that the SPRT not only reliably controls error probabilities but also typically requires substantially smaller samples than standard t tests and other common sequential designs. Moreover, we investigate the robustness of the SPRT against violations of its assumptions. Finally, we illustrate the sequential t test by applying it to an empirical example and provide recommendations on how psychologists can employ it in their own research to benefit from its desirable properties. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Sequential statistical procedures for processes of the exponential class with independent increments

Abstract: In this survey paper sequential statistical procedures for the exponential class of processes with independent increments are considered. After the definition of the exponential class a characterization in terms of the LEVY-CHXNTCBXN.representation is given. Then, sequential estimation problems* and sequential testing of hypotheses with respect to the underlying parameter are studied. Finally, some possible extensions of the considered notions and methods to other classes of processes are indicated.

Sequential modulation classification of dependent samples

Abstract: We classify the modulation scheme of a received signal waveform modeled by a finite state Markov chain. We compare the likelihood ratio test (LRT) known as a fixed-sample-size classifier, which uses a fixed amount of data, and the sequential probability ratio test (SPRT) known as a fixed-error-rate classifier, which uses a variable amount of data just enough to achieve a certain correct rate. The SPRT approach has several advantages, including reduced computational complexity, less decision delay, controllable classification error rate, etc. The performance of MPSK and trellis-coded modulation (TCM) classifiers are demonstrated.

Optimizing cervical specimen classifiers

Abstract: In an automated cervical cancer screening program, a prescreening machine could pass suspicious specimens to a cytotechnologist or cytopathologist while eliminating the normals from human consideration. This decision should be made at minimum cost consistent with acceptable false negative error rates. The sequential probability ratio test allows the designer to specify the probability of detection, select the false positive rate to minimize the overall cost of the test, and determine the expected cost of operating the system. The paper formulates that approach and presents specific examples based on actual cell classification experiments to illustrate the trade-off between operating cost and probability of detection.

Network Detection of Radiation Sources Using Localization-Based Approaches

Abstract: Radiation source detection is an important problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple “aggregated” area as a result of localization being enabled by the networked detectors. We propose the following three localization-based detection approaches: 1) source-attractor radiation detection (SRD); 2) triangulation-based radiation source detection (TriRSD); and 3) the ratio of square distance-based radiation source detection (ROSD-RSD). We use canonical datasets from Domestic Nuclear Detection Office's intelligence radiation sensors systems tests to assess the performance of these methods. Extensive results illustrate that SRD outperforms TriRSD and ROSD-RSD, and other existing detection algorithms based on the sequential probability ratio test and maximum likelihood estimation in terms of both false alarm and detection rates.