Models and Confidence Intervals for True Values in Interlaboratory Trials

Iyer, Hari; Wang, C. M. Jack; Mathew, Thomas

doi:10.1198/016214504000001682

Cited by 69 publications

(53 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 with Fig. 1 in Iyer et al (2004) shows that both intervals exhibit a similar and good performance, the coverage probability being always above the nominal level, with slight conservatism in some cases. We may note that GCI is somewhat more conservative than MSLRT in some cases with n i 5 (2, 2,y2) while it is the other way around in cases with a 5 2.…”

Section: Numerical Resultsmentioning

confidence: 63%

“…The HBK-MP interval is more satisfactory, even though the coverage probability does fall somewhat below the nominal level for small values of s; see Witkovsky´et al Savin et al (2003) and Iyer et al (2004), the intervals to be considered for practical use are the GCI, the intervals HBK-MP and KR-MP, and the MSLRT-based confidence interval proposed here. We shall also compute the different intervals for the example in the next section, in order to further see possible differences among them.…”

Section: Numerical Resultsmentioning

confidence: 96%

“…It is instructive to compare the performance of the MSLRT-based confidence interval with that of the GCI investigated in Iyer et al (2004). A comparison of our Fig.…”

Section: Numerical Resultsmentioning

confidence: 98%

See 2 more Smart Citations

Higher order inference for the consensus mean in inter-laboratory studies

Sharma

Mathew

2010

Biom. J.

Self Cite

View full text Add to dashboard Cite

In inter-laboratory studies, a fundamental problem of interest is inference concerning the consensus mean, when the measurements are made by several laboratories which may exhibit different within-laboratory variances, apart from the between laboratory variability. A heteroscedastic one-way random model is very often used to model this scenario. Under such a model, a modified signed log-likelihood ratio procedure is developed for the interval estimation of the common mean. Furthermore, simulation results are presented to show the accuracy of the proposed confidence interval, especially for small samples. The results are illustrated using an example on the determination of selenium in non-fat milk powder by combining the results of four methods. Here, the sample size is small, and the confidence limits for the common mean obtained by different methods produce very different results. The confidence interval based on the modified signed log-likelihood ratio procedure appears to be quite satisfactory.

show abstract

Section: Numerical Resultsmentioning

confidence: 63%

Section: Numerical Resultsmentioning

confidence: 96%

See 1 more Smart Citation

Higher order inference for the consensus mean in inter-laboratory studies

Sharma

Mathew

2010

Biom. J.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A generalized pivot for the mean in one-way random e ect model Iyer et al [12] developed a new generalized pivot for the mean in the one-way random e ect model with unequal sample sizes and heterogeneous variances. In the following, we brie y review this procedure.…”

Section: The Modelmentioning

confidence: 99%

Inferences on the within‐subject coefficient of variation

Tian

2005

Statistics in Medicine

View full text Add to dashboard Cite

The within-subject coefficient of variation (WCV) is widely used as a measure of precision and reproducibility of data in medical and biological science. In this paper, generalized confidence intervals and tests for a single WCV is developed using the concept of generalized pivots under the assumption of one-way random effect model. This approach is further extended to two-sample cases. The resulting procedures are easy to compute and have good properties in term of coverage probabilities and type-I error control at small sample sizes. The proposed methods are illustrated by a real life example.

show abstract

“…The concepts of generalized CI and generalized p-value have been widely applied to a variety of practical settings where standard methods are failed to produce satisfactory results. For example, see Weerahandi (1991Weerahandi ( , 1995b, Weerahandi and Johnson (1992), Zhou and Mathew (1994), Weerahandi and Berger (1999), Krishnamoorthy and Mathew (2003), Krishnamoorthy and Lu (2003), McNally et al (2003) and Iyer et al (2004). For a recipe of constructing generalized pivotal quantities (GPQs), see Iyer and Patterson (2002).…”

Section: Introductionmentioning

confidence: 99%

Inferences on the difference and ratio of the means of two inverse Gaussian distributions

Krishnamoorthy

Tian

2008

Journal of Statistical Planning and Inference

View full text Add to dashboard Cite

Methods for interval estimation and hypothesis testing about the ratio of two independent inverse Gaussian (IG) means based on the concept of generalized variable approach are proposed. As assessed by simulation, the coverage probabilities of the proposed approach are found to be very close to the nominal level even for small samples. The proposed new approaches are conceptually simple and are easy to use. Similar procedures are developed for constructing confidence intervals and hypothesis testing about the difference between two independent IG means. Monte Carlo comparison studies show that the results based on the generalized variable approach are as good as those based on the modified likelihood ratio test. The methods are illustrated using two examples.

show abstract

Models and Confidence Intervals for True Values in Interlaboratory Trials

Cited by 69 publications

References 2 publications

Higher order inference for the consensus mean in inter-laboratory studies

Higher order inference for the consensus mean in inter-laboratory studies

Inferences on the within‐subject coefficient of variation

Inferences on the difference and ratio of the means of two inverse Gaussian distributions

Contact Info

Product

Resources

About