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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.


Papers
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Proceedings ArticleDOI
26 Jun 2016
TL;DR: This paper addresses the problem of adaptive waveform design for target detection with composite sequential hypothesis testing with Bayesian considerations, and proposes a novel test, named penalized GSPRT (PGSPRT), on the basis of restraining the exponential growth of the GSP RT with respect to the sequential probability ratio test (SPRT).
Abstract: This paper addresses the problem of adaptive waveform design for target detection with composite sequential hypothesis testing. We begin with an asymptotic analysis of the generalized sequential probability ratio test (GSPRT). The analysis is based on Bayesian considerations, similar to the ones used for the derivation of the Bayesian information criterion (BIC) for model order selection. Following the analysis, a novel test, named penalized GSPRT (PGSPRT), is proposed on the basis of restraining the exponential growth of the GSPRT with respect to the sequential probability ratio test (SPRT). The performance measures of the PGSPRT in terms of average sample number (ASN) and error probabilities are also investigated. In the proposed waveform design scheme, the transmit spatial waveform (beamforming) is adaptively determined at each step based on observations in the previous steps. The spatial waveform is determined to minimize the ASN of the PGSPRT. Simulations demonstrate the performance measures of the new algorithm for target detection in a multiple input, single output channel.

4 citations

Journal ArticleDOI
TL;DR: In this article , a computational approach to the solution of the Kiefer-Weiss problem is presented, where algorithms for construction of the optimal sampling plans and evaluation of their performance are proposed.
Abstract: We present a computational approach to the solution of the Kiefer-Weiss problem.Algorithms for construction of the optimal sampling plans and evaluation of their performance are proposed. In the particular case of Bernoulli observations, the proposed algorithms are implemented in the form of R program code.Using the developed computer program, we numerically compare the optimal tests with the respective sequential probability ratio test (SPRT) and the fixed sample size test for a wide range of hypothesized values and type I and type II errors.The results are compared with those of D. Freeman and L. Weiss (Journal of the American Statistical Association, 59, 1964).The R source code for the algorithms of construction of optimal sampling plans and evaluation of their characteristics is available at https://github.com/tosinabase/Kiefer-Weiss.

4 citations

Journal ArticleDOI
TL;DR: In this paper, the problem of identifying a popula-tion with one of the two populations, with an aim to control both types of errors is studied, assuming that the populations are normal with unknown means, but with unit variance.
Abstract: In this paper we study the problem of identifying a popula-tion with one of the two populations, with an aim to control both types of errors We assume that the populations are normal with unknown means, but with unit variance We have cited examples from anthropological studies where our formulation of the problem fits in quite nicely We observe that SPRT’s based on the maximal invariant may not terminate with probability one Simulation studies reported here show a substantial saving in the average number of samples compared to the best invariant fixed sample test

4 citations

Proceedings ArticleDOI
18 Oct 2009
TL;DR: This paper presents a sequential misbehavior technique based on Sequential Probability Ratio Test (SPRT) for cooperative networks using automatic repeat request (ARQ) and evaluates performance of the detection technique both analytically and using numerical methods.
Abstract: Existing cooperative communications protocols are designed with the assumption that users always behave in a socially efficient manner. This assumption may be valid in networks under the control of a single authority where nodes cooperate efficiently to achieve a common goal. On the other hand, in commercial wireless networks where nodes are individually motivated to cooperate, the assumption that nodes will always obey rules of cooperation may not hold without implementing a mechanism to detect and mitigate misbehavior. In this paper, we present a sequential misbehavior technique based on Sequential Probability Ratio Test (SPRT) for cooperative networks using automatic repeat request (ARQ). We evaluate performance of the detection technique both analytically and using numerical methods.

4 citations

Journal Article
TL;DR: This work shows how the k-nearest neighbor classification algorithm in machine learning can be utilized as a mathemati- cal framework to derive a variety of novel sequential sampling models and proposes a common mathe- matical framework combining these methods and providing a systematic explanation for understanding different methods.

4 citations


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