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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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Journal Article
TL;DR: In this article, the principle idea of the fundamental lemma of Neyman and Pearson is applied to interpret the goodness of tests, as well as retrospective and sequential change point detections are considered in the context of the proposed technique.
Abstract: Commonly, in accordance with a given risk-function of hypothesis testing, investigators try to derive an optimal property of a test. This paper demonstrates that criteria for which a given test is optimal can be declared by the structure of this test, and hence almost any reasonable test is optimal. In order to establish this conclusion, the principle idea of the fundamental lemma of Neyman and Pearson is applied to interpret the goodness of tests, as well as retrospective and sequential change point detections are considered in the context of the proposed technique. Aside from that, the present article evaluates a specific classification problem that corresponds to measurement error effects in occupational medicine.

8 citations

Proceedings ArticleDOI
01 Nov 2015
TL;DR: This work proposes a sequential canonical correlation technique (S-CCT) method to estimate the number of active PUs quickly and accurately, and shows that the method can achieve better performance with fewer samples than CCT.
Abstract: In cognitive radio networks, a priori information on the number of primary users (PUs) is helpful to estimate more specific parameters of PUs' signal, such as the carrier frequency, direction of arrival, and location. We propose a sequential canonical correlation technique (S-CCT) method to estimate the number of active PUs quickly and accurately. In the proposed method, classical canonical correlation technique (CCT) is improved using multi-hypothesis sequential probability ratio test. Simulation results show that our proposed S-CCT method can achieve better performance with fewer samples than CCT.

8 citations

24 Sep 2014
TL;DR: This work develops a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model and proposes an efficient plane propagation mechanism that leverages the pre-computed sampling positions and the local structure model described by the reduced local disparity set.
Abstract: : We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model. We operate over local image neighborhoods by iteratively estimating single pixel disparity values until sufficient evidence has been gathered to either validate or contradict the current hypothesis regarding local scene structure. The output of our sampling is a set of sampled pixel positions along with a robust and compact estimate of the set of disparities contained within a given region. We further propose an efficient plane propagation mechanism that leverages the pre-computed sampling positions and the local structure model described by the reduced local disparity set. Our sampling framework is a general pre-processing mechanism aimed at reducing computational complexity of disparity search algorithms by ascertaining a reduced set of disparity hypotheses for each pixel. Experiments demonstrate the effectiveness of the proposed approach when compared to state of the art methods.

8 citations

Journal ArticleDOI
TL;DR: Two models for modified, one-sample, sequential probability ratio tests based on Lehmann alternatives are considered, one developed by Weed and Bradley and one by Govindarajulu as discussed by the authors.
Abstract: Two models for modified, one-sample, sequential probability ratio tests based on Lehmann alternatives are considered, one developed by Weed and Bradley and one by Govindarajulu. It is shown how they are related. Sure termination of the SPRT's is established under very general conditions.

8 citations

01 Jan 1966
TL;DR: In this paper, it was shown that the variance of the sample number is approximately proportional to the square of the average sample number, and that the distribution of sample number variance in sequential probability ratio tests is a function of the number of samples in the test set.
Abstract: Little published information is available about moments, other than the first, of the distribution of the sample number in Wald type sequential probability ratio tests. Empirical evidence from Monte Carlo studies is presented for the inference that the variance of the sample number is approximately proportional to the square of the average sample number.

8 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20236
202223
202129
202023
201929
201832