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.

Sprt and cusum results for inverse gaussian processes

Abstract: The inverse gaussian distribution is a flexible model with applications in generalized linear models and accelerated life testing where early failure times predominate. Recently it has received attention in quality improvement and as a model for which an alternative to the analysis of variance, known as analysis of reciprocals, exists. In reliability testing and acceptance sampling data acquisition is often resource inhibited and may be both costly and time-consuming. In such settings it is desirable to reach a statistically sound decision as quickly as possible. Based on sequential probability ratio tests (SPRT), sequential sampling/ cumulative sum (CUSUM) plans allow for timely, statistically based decisions. SPRT and CUSUM results for the inverse gaussian process mean when the value of the shape parameter of the density is known are presented in this paper.

On Sequential Data-Driven Density Estimation

Abstract: The theory and methods of minimax and sequential inferences, pioneered by Abraham Wald in 1940's, shaped the way statisticians see the statistics today. This article employs the Wald approaches together with the modern oracle analysis to develop the theory and methods of a sharp minimax adaptive sequential density estimation. In particular, it proves a long-standing conjecture about a sufficient condition for a sharp adaptive sequential estimation with an assigned mean integrated squared error. It also suggests, and then studies via intensive Monte-Carlo simulations, a data-driven sequential density estimator that can be recommended for practical applications.

Testability demonstration for a flight control system based on sequential probability ratio test method

Abstract: The flight control system plays an important role in adjusting the attitude of manned or auto-pilot aircrafts. To reduce the fault diagnosis time and accelerate the maintenance actions, many flight control systems have adopted the design for testability. Testability demonstration for the flight control system is needed to check the indexes of testability such as fault detection rate and fault isolation rate. Currently, the standards and statistical methods for the testability demonstration planning have the problems such as large sample, long test period and it is not optimal for the flight control systems which are of complex structure and high cost. A testability demonstration planning method based on the sequential probability ratio test method is proposed as it can decrease the sample size with almost the same operation characteristic as the classical method. Firstly, the decision factor and rules of the sequential probability ratio test method and truncated decision rules are introduced. Secondly, th...

Fault detection and isolation based on bond graph modeling and empirical residual evaluation

Abstract: Fault detection and isolation are critical for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. In order to realize a robust and real time monitoring and diagnosis for these types of multi-energy domain systems, this paper presents a novel scheme that integrates bond graph modeling for fault signatures establishment, and a multivariate state estimation technique-based empirical estimation for residual generation followed by a Sequential Probability Ratio Test-based residual evaluation for monitoring alarm. Once a fault is detected and alerted, a synthesized non-null coherence vector is created, and then matched with the pre-designed fault signatures matrix to isolate possible faults. To identify the effectiveness of the proposed methodology, a simulation for pneumatic equalizer control unit of locomotive electronically controlled pneumatic brake is conducted. The experimental results show that satisfied performance of fault detection and isolation can be...

Asymptotically Optimal Stochastic Encryption for Quantized Sequential Detection in the Presence of Eavesdroppers

Abstract: We consider sequential detection based on quantized data in the presence of eavesdropper. Stochastic encryption is employed as a counter measure that flips the quantization bits at each sensor according to certain probabilities, and the flipping probabilities are only known to the legitimate fusion center (LFC) but not the eavesdropping fusion center (EFC). As a result, the LFC employs the optimal sequential probability ratio test (SPRT) for sequential detection whereas the EFC employs a mismatched SPRT (MSPRT). We characterize the asymptotic performance of the MSPRT in terms of the expected sample size as a function of the vanishing error probabilities. We show that when the detection error probabilities are set to be the same at the LFC and EFC, every symmetric stochastic encryption is ineffective in the sense that it leads to the same expected sample size at the LFC and EFC. Next, in the asymptotic regime of small detection error probabilities, we show that every stochastic encryption degrades the performance of the quantized sequential detection at the LFC by increasing the expected sample size, and the expected sample size required at the EFC is no fewer than that is required at the LFC. Then the optimal stochastic encryption is investigated in the sense of maximizing the difference between the expected sample sizes required at the EFC and LFC. Although this optimization problem is nonconvex, we show that if the acceptable tolerance of the increase in the expected sample size at the LFC induced by the stochastic encryption is small enough, then the globally optimal stochastic encryption can be analytically obtained; and moreover, the optimal scheme only flips one type of quantized bits (i.e., 1 or 0) and keeps the other type unchanged.