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 Test of Fuzzy Hypotheses

Abstract: In testing statistical hypotheses, as in other statistical problems, we may be confronted with fuzzy concepts. This paper deals with the problem of testing hypotheses, when the hypotheses are fuzzy and the data are crisp. We first give new definitions for notion of mass (density) probability function with fuzzy parameter, probability of type I and type II errors and then state and prove the sequential probability ratio test, on the basis of these new errors, for testing fuzzy hypotheses. Numerical examples are also provided to illustrate the approach.

A neural-network Q-learning method for decentralized sequential detection with feedback

Abstract: This paper studies a feedback decentralized sequential detection system using Q-learning. The purpose is to obtain a better understanding of certain distributed detection systems and to examine the impact of feedback information. Performance comparisons are made in the paper between the Q-learning approach, the centralized sequential probability ratio test method, a dynamic programming method, and a non-feedback distributed detection method.

Pre-checking for non-monotonicity of the Wald statistic

Abstract: The non-monotonic behaviour of the Wald test in some finite-sample applications leads to low power when the null hypothesis needs rejection most. This article proposes a simple check for discerning if the Wald statistic for testing significance of regression coefficients is non-monotonic in the neighbourhood of the parameter space from which the sample data are drawn. Monte Carlo simulations show that this method works rather well for detecting situations where the Wald test can be safely applied. An example is provided to illustrate the use of this check.

Efficient reinforcement learning in parameterized models: discrete parameters

Abstract: We consider reinforcement learning in a parameterized setup, where the controlled model is known to belong to a finite set of Markov Decision Processes (MDPs) under the discounted return criteria. We propose an on-line algorithm for learning in such parameterized models, called the Parameter Elimination (PEL) algorithm, and analyze its performance in terms of the the total mistake bound criterion, which upper-bounds the total number of suboptimal actions performed by the algorithm over the infinite time horizon. The proposed algorithm relies on Wald's Sequential Probability Ratio Test to eliminate unlikely parameters, and uses an optimistic policy for effective exploration. We establish that, with high probability, the total mistake bound for the algorithm is linear (up to a logarithmic term) in the cardinality |Θ| of the parameter set, independently of the cardinality of the state and action spaces. We further demonstrate that much better dependence |Θ| may be obtained for this algorithm, depending on the specific information structure of the problem.

Minimizing Detection Time While Guaranteeing Desired Detection Accuracy in Cooperative Primary User Detection

Abstract: This paper investigates the issue of minimizing the detection time while guaranteeing the desired detection accuracy in cooperative primary user detection for cognitive radio. To achieve this goal, the sequential cooperative energy detection (SCED) scheme is firstly introduced. This scheme implements sequential probability ratio test to make final decisions and can guarantee the desired detection accuracy with less detection time compared with other schemes. Then, detection time in the SCED scheme is minimized by choosing an optimum sample number. Expressions for the optimum sample number and the minimum detection time in four typical scenarios are also derived. Simulation results are presented to illustrate the benefits of the SCED scheme and to examine the optimum sample number as well as the minimum detection time.