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The intraclass correlation coefficient ρ is used as a measure of the degree of resemblance among family members with respect to some biological or psychological attribute, such as blood pressure level or IQ. Many studies have been done in the area. However, a significant gap in the literature is that few statistics are available to test the significance ( H 0 : ρ = 0 against H A : ρ ≠ 0 ) of this important biological measure and no study is available as to the performance of these statistics with respect to level and power. In this paper we develop three large sample test statistics and compare these, in terms of level and power, using extensive simulation studies of these, the Wald test developed in this paper is found to have a very simple form and highest power while maintaining level well. [ABSTRACT FROM AUTHOR]

The log-Birnbaum-Saunders regression model introduced by Rieck and Nedelman (1991) is useful for modeling lifetimes of materials and equipments subject to different conditions. Our goal in this article is twofold. First, we numerically evaluate the finite sample performances of the likelihood ratio, score and Wald tests in the log-Birnbaum-Saunders regression model. Second, we introduce a RESET-like misspecification test for that model. The null hypothesis is that the model is correctly specified which is tested against the alternative hypothesis of model misspecification. The power of the test is evaluated using Monte Carlo simulations. Bootstrap-based inference is also considered. An empirical application is presented and discussed. [ABSTRACT FROM AUTHOR]

One standard summary of a clinical trial is a confidence limit for the effect of the treatment. Unfortunately, standard approximate limitsmay have poor frequentist properties, even for quite large sample sizes. It has been known since Buehler (1957) that an imperfect confidence limit can be adjusted to have exact coverage. These "tight" limits are the gold standard frequentist confidence limit. Computing tight limits requires exact calculation of certain tail probabilities and optimisation of potentially erratic functions of the nuisance parameter. Naive implementation is both computationally unreliable and highly burdensome, and perhaps explains why they are not in common use. For clinical trials however, where the data and parameter have dimension two, the difficulties can be fully surmounted. This paper brings together several results in the area and applies them to simple two dimensional problems. It is shown how to reduce the computational burden by an order of magnitude. Difficulties with the optimisation reliability are mitigated by applying two different computational strategies,which tend to break down under different conditions, and taking the less stringent of the two computed limits. This paper specifically develops limits for the relative risk in a clinical trial, but it should be clear to the reader that the method extends to arbitrary measures of treatment effect without essential modification. [ABSTRACT FROM AUTHOR]

This study aims to provide a reliable confidence interval for assessing the process incapability index [Cpp]. The concept of the generalized pivotal quantities is utilized for constructing the generalized confidence interval for [Cpp]. And, simulations are performed for demonstrating our proposed method and one existent method. The results show that the empirical confidences of these two methods are significantly affected by the degree of process departure. Therefore, we suggest the practitioners to select proper one for capability testing purpose based on the information of degree of process departure. [ABSTRACT FROM AUTHOR]

In this study, we consider the construction of the confidence interval for the population proportion while using group testing with misclassification. We propose two confidence intervals based on Cornish-Fisher expansion and a modified Wilson's interval based on a newly developed estimator. We investigate the performance of these intervals extensively and also apply the methods to real datasets. Our newly derived methods have competitive performance compared with existing methods. [ABSTRACT FROM AUTHOR]

Test statistics from the class of two-sample linear rank tests are commonly used to compare a treatment group with a control group. Two independent random samples of sizes m and n are drawn from two populations. As a result, N = m + n observations in total are obtained. The aim is to test the null hypothesis of identical distributions. The alternative hypothesis is that the populations are of the same form but with a different measure of central tendency. This article examines mid p-values from the null permutation distributions of tests based on the class of two-sample linear rank statistics. The results obtained indicate that normal approximation-based computations are very close to the permutation simulations, and they provide p-values that are close to the exact mid p-values for all practical purposes. [ABSTRACT FROM AUTHOR]