A MODIFICATION OF THE SEQUENTIAL PROBABILITY RATIO TEST FOR TESTING A NORMAL MEAN

A modification of the sequential probability ratio test is proposed in which Wald's parallel boundaries are broken at some preassigned point of the sample number axis and Anderson's converging boundaries are used prior to that. Read's partial sequential probability ratio test can be considered as a special case of the proposed procedure. As far as 'the maximum average sample number reducing property is concerned, the procedure is as good as Anderson's modified sequential probability ratio test.     

A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates

We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER). Together, we call these conditions a ‘rejection principle for sequential tests’, which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next, the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing. Examples of these new procedures are given by applying them to a chromosome aberration data set and finding the maximum safe dose of a treatment.     

Sequential confidence intervals for the number of cells in a multinomial population

A two-sided sequential confidence interval is suggested for the number of equally probable cells in a given multinomial population with prescribed width and confidence coefficient. We establish large-sample properties of the fixed-width confidence interval procedure using a normal approximation, and some comparisons are made. In addition, a simulation study is carried out in order to investigate the finite sample behaviour of the suggested sequential interval estimation procedure.     

ASYMPTOTIC EXPANSIONS APPLIED TO F-TEST CRITERIA

Using asymptotic expansions of the Kummer hypergeometric function, the sequential. F-test criterion is evaluated asymptotically in terms of the sample size. The continuation region inequalities for the test are inverted and expressed in terms of known test criteria. A rapidly converging algorithm for carrying out the sequential procedure is provided. This makes the F-test easier for the practitioner to use. Almost sure finite termination of the sequential. F-test is asserted by appealing to the continuation inequalities and a heuristic asymptotic expansion of the test criterion. Average stopping times of the sequential procedure for a variety of population means and population number configurations are tabulated. The computer symbolic manipulation program MAPLE was used to derive some formulae.     

Modified d-optimal sequential procedure in allocation problem

When an experimenter wishes to compare t treatments with M experimental units, one of the first problems he faces is how to allocate N experimental units into t treatments. When no pre treat merit information about the experimental units is available, "randomization" is the widely accepted guiding principle to deal with the allocation problem But pre treat merit information usually is available, although it is seldom fully used for allocation purposes. Recently, Harville considered the allocation problem under a covariance model. He suggested a D-optimal sequential procedure that may be used to construct nearly D-optimal allocations. However, Harville's sequential procedure requires constructing a D-optimal initial allocation at the first stages and that may be computationally unfeasible in some real situations, Such construction is not needed for a new sequential.     

Asymptotic optimality of a two-stage procedure in Bayes sequential estimation

The problem of sequential estimation of the mean with quadratic loss and fixed cost per observation is considered within the Bayesian framework. Instead of fully sequential sampling, a two-stage sampling technique is introduced to solve the problem. The proposed two-stage procedure is robust in the sense that it does not depend on the distribution of outcome variables and the prior. It is shown to be asymptotically not worse than the optimal fixed-sample-size procedures for the arbitrary distributions, and to be asymptotically Bayes for the distributions of one-parameter exponential family.     

A two-sample parametric group sequential procedure for monitoring clinical trials

A group sequential procedure is presented which allows for staggered entry of patients, random loss to followup, and utilizes the flexible boundary approach of Lan and DeMets. The proposed procedure assumes that response times are nearly exponentially distributed. An example as well as simulation studies comparing the performance of the proposed procedure with the group sequential logrank are also presented.     

Distribution of the Stopping Time in Bayesian Sequential Sampling

The paper presents a method for computing the distribution of the stopping time for the Bayesian sequential one step look-ahead procedure with the proportion as parameter.     

Choosing between the BP and BN sequential strategies

Cost and burden of diagnostic testing may be reduced if fewer tests can be applied. Sequential testing involves selecting a sequence of tests, but only administering subsequent tests dependent on results of previous tests. This research provides guidance to choosing between single tests or the believe the positive (BP) and believe the negative (BN) sequential testing strategies, using accuracy (as measured by the Youden Index) as the primary determinant. Approximately 75% of the parameter combinations examined resulted in either BP or BN being recommended based on a higher accuracy at the optimal point. In about half of the scenarios BP was preferred, and the other half, BN, with the choice often a function of the value of the ratio of standard deviations of those without and with disease (b). Large values of b for the first test of the sequence tended to be associated with preference for BN as opposed to BP, while small values of b appear to favor BP. When there was no preference between sequences and/or single tests based on the Youden Index, cost of the sequence was considered. In this case, disease prevalence plays a large role in the selection of strategies, with lower values favoring BN and sometimes higher values favoring BP. The cost threshold for the sequential strategy to be preferred over a single, more accurate test, was often quite high. It appears that while sequential strategies most often increase diagnostic accuracy over a single test, sequential strategies are not always preferred.     

A sequential method for the evaluation of the VaR model based on the run between exceedances

Summary: A technique for sequential assessment of the appropriateness of the VaR model is developed, by drawing on a tool from statistical process control, namely the control chart. We show that an EWMA control chart is the most appropriate instrument for detecting changes in the process of the magnitude of interest in risk management. The robustness of our procedure with respect to violations of some assumptions is examined and it is concluded that our model evaluation technique remains suitable in such cases.     

A generalization of anderson's modified sequential probability ratio test

A generalization of Anderson's sequential probability ratio test procedure is proposed in which the continuation region is bounded by a pair of converging lines up to a certain stage of the experiment and later by another pair of converging lines until the procedure is truncated at a predetermined stage of the experiment. The OC and the ASN functions have been derived. For certain parameter values the proposed procedure attains lower average sample numbers than that attainable by any other known procedure.     

Controlling Type-I Error Rate in Monitoring Structural Changes Using Partially Sequential Procedures

In the present article, we develop some asymptotically power on partially sequential nonparametric tests for monitoring structural changes. Our test procedures are based on Wilcoxon score. We use the idea of curved stopping boundaries. We derive some exact results and perform simulation studies to provide various properties of the tests. We see that one of the proposed procedures significantly controls the Type I error rate. This procedure may be very effective for fluctuation monitoring. We illustrate the procedures by using real life data from the stock market.     

Optimal allocations in sequential tests involving two populations with covariates

We consider the problem of testing which of two normally distributed treatments has the largest mean, when the tested populations incorporate a covariate. From the class of procedures using the invariant sequential probability ratio test we derive an optimal allocation that minimizes, in a continuous time setting, the expected sampling costs. Simulations show that this procedure reduces the number of observations from the costlier treatment and categories while maintaining an overall sample size closer to the “pairwise” procedure. A randomized trial example is given.     

On a class of partially sequential two-sample test procedures for multivariate continuous data

In a two-sample testing problem, sometimes one of the sample observations are difficult and/or costlier to collect compared to the other one. Also, it may be the situation that sample observations from one of the populations have been previously collected and for operational advantages we do not wish to collect any more observations from the second population that are necessary for reaching a decision. Partially sequential technique is found to be very useful in such situations. The technique gained its popularity in statistics literature due to its very nature of capitalizing the best aspects of both fixed and sequential procedures. The literature is enriched with various types of partially sequential techniques useable under different types of data set-up. Nonetheless, there is no mention of multivariate data framework in this context, although very common in practice. The present paper aims at developing a class of partially sequential nonparametric test procedures for two-sample multivariate continuous data. For this we suggest a suitable stopping rule adopting inverse sampling technique and propose a class of test statistics based on the samples drawn using the suggested sampling scheme. Various asymptotic properties of the proposed tests are explored. An extensive simulation study is also performed to study the asymptotic performance of the tests. Finally the benefit of the proposed test procedure is demonstrated with an application to a real-life data on liver disease.     

Estimating Process Capability Index C PM Using a Bootstrap Sequential Sampling Procedure

Construction of a confidence interval for process capability index C _PM is often based on a normal approximation with fixed sample size. In this article, we describe a different approach in constructing a fixed-width confidence interval for process capability index C _PM with a preassigned accuracy by using a combination of bootstrap and sequential sampling schemes. The optimal sample size required to achieve a preassigned confidence level is obtained using both two-stage and modified two-stage sequential procedures. The procedure developed is also validated using an extensive simulation study.     

Tables for pairwise multiple comparisons using shaffer's modifiedsequentially-rejective procedure

A sequentially rejective (SR) testing procedure introduced by Holm (1979) and modified (MSR) by Shaffer (1986) is considered for testing all pairwise mean comparisons.For such comparisons, both the SR and MSR methods require that the observed test statistics be ordered and compared, each in turn, to appropriate percentiles on Student's t distribution.For the MSR method these percentiles are based on the maximum number of true null hypotheses remaining at each stage of the sequential procedure, given prior significance at previous stages, A function is developed for determining this number from the number of means being tested and the stage of the test.For a test of all pairwise comparisons, the logical implications which follow the rejection of a null hypothesis renders the MSR procedure uniformly more powerful than the SR procedure.Tables of percentiles for comparing K means, 3 < K < 6, using the MSR method are presented.These tables use Sidak's (1967) multiplicative inequality and simplify the use of t he MSR procedure.Several modifications to the MSR are suggested as a means of further increasing the power for testing the pairwise comparisons.General use of the MSR and the corresponding function for testing other parameters besides the mean is discussed.     

Estimation of the Spectral Density with Assigned Risk

It is well known that adaptive sequential nonparametric estimation of differentiable functions with assigned mean integrated squared error and minimax expected stopping time is impossible. In other words, no sequential estimator can compete with an oracle estimator that knows how many derivatives an estimated curve has. Differentiable functions are typical in probability density and regression models but not in spectral density models, where considered functions are typically smoother. This paper shows that for a large class of spectral densities, which includes spectral densities of classical autoregressive moving average processes, an adaptive minimax sequential estimation with assigned mean integrated squared error is possible. Furthermore, a two‐stage sequential procedure is proposed, which is minimax and adaptive to smoothness of an underlying spectral density.     

The use of local and nonlocal priors in Bayesian test-based monitoring for single-arm phase II clinical trials

Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive probabilities and Bayesian hypothesis testing procedures using Bayes factors. In the first part of the paper, we briefly show the connections between test-based (TB) and posterior probability-based (PB) sequential monitoring approaches. Next, we extensively investigate the choice of local and nonlocal priors for the TB monitoring procedure. We describe the pros and cons of different priors in terms of operating characteristics. We also develop a user-friendly Shiny application to implement the TB design.     

Sequential Procedure For Fixed Accuracy Estimation of the Population Size in Recapture Sampling

A sequential procedure is constructed to provide a fixed‐accuracy estimator for the number of faults in a system. This paper focuses on the case when faults are homogeneous. However, the method can be adapted to other models by choosing a more robust estimator. The accuracy of the estimator depends on the failure intensity, the length of testing period and the total number of faults in the system. Simulations illustrate the performance of the proposed procedure. The method is applied to an information system failure dataset.