Topic
Coverage probability
About: Coverage probability is a research topic. Over the lifetime, 2479 publications have been published within this topic receiving 53259 citations.
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TL;DR: In this paper, the authors consider and propose some confidence intervals for estimating the mean or difference of means of skewed populations, and extend the median t interval to the two sample problem, and suggest using the bootstrap to find the critical points for use in the calculation of median t intervals.
Abstract: In this paper we consider and propose some confidence intervals for estimating the mean or difference of means of skewed populations. We extend the median t interval to the two sample problem. Further, we suggest using the bootstrap to find the critical points for use in the calculation of median t intervals. A simulation study has been made to compare the performance of the intervals and a real life example has been considered to illustrate the application of the methods.
19 citations
TL;DR: In this article, the authors proposed a new method of calibration for the empirical loglikelihood ratio, which corrects the undercoverage problem of the χ 2-approximation.
Abstract: Summary Empirical likelihood has attracted much attention in the literature as a nonparametric method. A recent paper by Lu & Peng (2002)[Likelihood based confidence intervals for the tail index. Extremes 5, 337–352] applied this method to construct a confidence interval for the tail index of a heavy-tailed distribution. It turns out that the empirical likelihood method, as well as other likelihood-based methods, performs better than the normal approximation method in terms of coverage probability. However, when the sample size is small, the confidence interval computed using the χ 2 approximation has a serious undercoverage problem. Motivated by Tsao (2004)[A new method of calibration for the empirical loglikelihood ratio. Statist. Probab. Lett. 68, 305–314], this paper proposes a new method of calibration, which corrects the undercoverage problem.
19 citations
TL;DR: In this article, three simple interval estimates for the risk ratio in inverse sampling are considered, and the first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively.
Abstract: Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.
19 citations
TL;DR: A novel hierarchical Bayesian method is developed to make statistical inference simultaneously on the threshold and the treatment effect restricted on the sensitive subset defined by the biomarker threshold, which provides better finite sample properties in terms of the coverage probability of a 95% credible interval.
19 citations
TL;DR: Methods based on normal approximations and generalized pivotal quantities are extended to the case of multiple contrasts and are found to be very liberal and biased with respect to directional type-I-errors.
19 citations