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.

Elastic modulus in large concrete structures by a sequential hypothesis testing procedure applied to impulse method data

Abstract: An experimental method denoted as Impulse Method is proposed as a cost-effective non-destructive technique for the on-site evaluation of concrete elastic modulus in existing structures: on the basis of Hertz`s quasi-static theory of elastic impact and with the aid of a simple portable testing equipment, it makes it possible to collect series of local measurements of the elastic modulus in an easy way and in a very short time. A Hypothesis Testing procedure is developed in order to provide a statistical tool for processing the data collected by means of the Impulse Method and assessing the possible occurrence of significant variations in the elastic modulus without exceeding some prescribed error probabilities. It is based on a particular formulation of the renowned sequential probability ratio test and reveals to be optimal with respect to the error probabilities and the required number of observations, thus further improving the time-effectiveness of the Impulse Method. The results of an experimental investigation on different types of plain concrete prove the validity of the Impulse Method in estimating the unknown value of the elastic modulus and attest the effectiveness of the proposed Hypothesis Testing procedure in identifying significant variations in the elastic modulus.

Structural Change Detection of Time Series Using Sequential Probability Ratio Test

Abstract: Time series analysis is used in various fields such as not only in economics but also in pattern recognition, biometrics, and Kansei engineering field. The problem of predicting time series can be classified into three in a practical sense. The first problem is how to make a model for prediction, that adequately represents the characteristics of the past time series data. The second problem is how to correctly detect the structural change of the time series as soon as possible, when the estimated prediction model does not meet the real data. The third problem is how to quickly find the new prediction model to meet the real data after the structural change. This paper focuses on the second problem and proposes a method based on a probability ratio test that has been used in the field of the quality control. This paper also shows some experimental results comparing with a conventional method, and presents the effectiveness of the proposed method.

Further properties of a sequential test of fit.

Abstract: : In earlier papers, a sequential test of fit was proposed and examined. In the present paper, the expected sample size under alternatives to the null hypothesis is investigated, and a modification of the test is proposed which is more easily compared to other tests. (Author)

A two- sample partially sequential probability ratio test

Abstract: A two-sample partially sequential probability ratio test (PSPRT) is considered for the two-sample location problem with one sample fixed and the other sequential. Observations are assumed to come from two normal poptilatlons with equal and known variances. Asymptotically in the fixed-sample size the PSPRT is a truncated Wald one sample sequential probability test. Brownian motion approximations for boundary-crossing probabilities and expected sequential sample size are obtained. These calculations are compared to values obtained by Monte Carlo simulation.

A NOTE ON A SEQUENTIAL GROUP t-TEST

Abstract: A sequential probability ratio test (SPET) of the mean of a normal distribution with unknown variance, based on an independent sequence of groups of observations, is investigated and its efficiency compared with that of the WAGE sequential t-test, which is based on an invariantly sufficient sequence of test statistics.