A group sequential test for the inverse Gaussian mean

Bacanli, Sevil; Demirhan, Yaprak Parlak

doi:10.1007/s00362-006-0020-9

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…Table 3 gives the maximum sample sizes and the expected sample sizes for the fixed sample, the sequential probability ratio, and the group sequential test. Other examples can be presented that have the same general principle with different choices of α,β,µ 0 ,µ 1 and κ. Pocock [15] compared those tests for the mean of the normal distribution and showed that GST is more advantageous than the fixed sample test and SPRT in terms of sample size; in addition , Bacanlı & Demirhan [1] proposed the group sequential test for the mean of the inverse Gaussian distribution, in a similar way and showed that this test is more advantageous than the others.…”

Section: Group Sequential Test For the Mean Direction Of The Von Misementioning

confidence: 99%

A Group Sequential Test of Circular Data Using the Von Mises Distribution

Peker

Bacanli

2014

HJMS

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In this study, the group sequential test is suggested for the mean direction parameter of the von Mises distribution when the concentration parameter is known and unknown. An application of the proposed test is illustrated by using a medical data of the patients, who were complained about internal rotation angles of the shoulder and treated in a rehabilitation and physical therapy center in Eskiehir, Turkey. It is shown that the results of the study demonstrate that the group sequential test can provide a great advantage not only for linear data but also for circular data in terms of sample size.

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Section: Group Sequential Test For the Mean Direction Of The Von Misementioning

confidence: 99%

A Group Sequential Test of Circular Data Using the Von Mises Distribution

Peker

Bacanli

2014

HJMS

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“…Group sequential tests can also be used in an experiment with only one treatment in which the response results are compared with a known standard. For a normal response with a known variance and 𝐻 0 hypothesis mean 𝜇 0 , the critical values 𝑍 𝑘 and Δ become √𝑛(𝜇 1 −𝜇 0 )/𝜎 [17,22]. When 𝐾 = 1, that is, for fixed sample size test, (17) becomes the classical hypothesis test for normal response.…”

Section: Advances In Fuzzy Systemsmentioning

confidence: 99%

Fuzzy Approach for Group Sequential Test

Idotçen

Bacanli

Günay

2014

Advances in Fuzzy Systems

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Buckley’s approach (Buckley (2004), (2005), (2006)) uses sets of confidence intervals by taking into consideration both of the uncertainty and impreciseness of concepts that produce triangular shaped fuzzy numbers for the estimator. This approach produces fuzzy test statistics and fuzzy critical values in hypothesis testing. In addition, the sample size is fixed for this test. When data comes sequentially, however, it is not suitable to study with a fixed sample size test. In such cases, sequential and group sequential tests are recommended. Unlike a sequential test, a group of sequential test provides substantial savings in sample and enables us to make decisions as early as possible. This intends paper to combine the benefits of group sequential test and Buckley's approach usingα-cuts. It attempts to show that usingα-cuts can be used within the group sequential tests. To illustrate the test more explicitly a numerical example is also given.

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“…Here, our aim is to compare the mean vectors of two continuous time multidimensional Gaussian processes (see, e.g., Akahira 2002;Bacanli and Demirhan 2008;Salau 2003) based on the observation of both paths with nuisance parameters in the covariances. There currently exist quite many continuous multivariate processes in Economy, Physics, or Medicine (among others) which follow Gaussian processes in continuous time.…”

Section: Introductionmentioning

confidence: 99%

Generalized p value for multivariate Gaussian stochastic processes in continuous time

2017

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We construct a Generalized p value for testing statistical hypotheses on the comparison of mean vectors in the sequential observation of two continuous time multidimensional Gaussian processes. The mean vectors depend linearly on two multidimensional parameters and with different conditions about their covariance structures. The invariance of the generalized p value considered is proved under certain linear transformations. We report results of a simulation study showing power and errors probabilities for them. Finally, we apply our results to a real data set.

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A group sequential test for the inverse Gaussian mean

Abstract: Inverse Gaussian distribution, Sequential test, Group sequential test,

Cited by 8 publications

References 10 publications

A Group Sequential Test of Circular Data Using the Von Mises Distribution

A Group Sequential Test of Circular Data Using the Von Mises Distribution

Fuzzy Approach for Group Sequential Test

Generalized p value for multivariate Gaussian stochastic processes in continuous time

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