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.

Quantization for Sequential Signal Detection

Abstract: The quantization of the observed data for sequential signal detection is studied. The criteria used are the minimizations of the average sample number under the hypothesis, the average sample number under the alternative, and the maximum average sample number. Numerical results show that the performance is not very sensitive to different criteria. Using a sequential probability ratio test (SPRT), the performances of optimum quantizers are compared to systems with unquantized data. The asymptotic relative efficiencies of the quantizerSPRT's with respect to the SPRT for unquantized data are derived for symmetric noise densities. The relation between these asymptotic relative efficiencies and those of fixed-sample-size detectors is noted.

A Signal-to-Noise Property of Binary Decisions

Abstract: THE statistical theory of Neyman and Pearson1, which uses the likelihood ratio in making binary decisions, has proved very valuable when applied to the physical problem of detecting a signal in noise2. This theory uses error probabilities as the basic criterion of performance in detection and tends to suggest that the attention given to the signal-to-noise ratio in older approaches is now outmoded. The purpose of this communication is to point out that, while a maximum signal-to-noise property is not any longer to be considered necessary in an optimum receiver for detection, a suitably formulated signal-to-noise requirement is sufficient to lead to a likelihood ratio receiver. This conclusion rests on a fully general property of the Neyman–Pearson binary decision theory which seems to have escaped previous notice.

Sequential sampling for incidence of phomopsis leaf blight of strawberry.

Abstract: Sequential sampling models for estimation and classification were developed for the incidence of strawberry leaflets infected by Phomopsis obscurans. Sampling protocols were based on a binary power law analysis of the spatial heterogeneity of Phomopsis leaf blight in commercial fields in Ohio. For sequential estimation, samples were collected until mean disease incidence could be estimated with a preselected coefficient of variation of the mean (C). For sequential classification, samples were collected until there was sufficient evidence to classify mean incidence as being below or above a threshold (pt) based on the sequential probability ratio test. Monte-Carlo simulations were used to determine the theoretical average sample number (ASN) and probability of classifying mean incidence as less than pt (operating characteristic) for any true value of incidence. Estimation and classification sampling models were both tested with bootstrap simulations of randomly selected data sets and validated by ...

A continuous-time distributed version of wald's sequential hypothesis testing problem

Abstract: This paper discusses a distributed version of Wald’s sequential hypothesis testing problem in the continuous time framework. For sake of concreteness, two decision-makers equipped with their own sensors, are faced with the following hypothesis testing problem: Decide between hypothesis H 0 and H 1, where with μ i ≠ 0 and σ i ≠ 0, i = 1, 2, non-random; here the noises {W t 1 , t ≥ 0} and {W t 2 , t ≥ 0} are independent Brownian motions.