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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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Journal ArticleDOI
TL;DR: In this paper, four asymptotic interval estimators were proposed for the case of no confounders, and Monte Carlo simulation was employed to evaluate the finite-sample performance of these estimators in a variety of situations.
Abstract: Since it can account for both the strength of the association between exposure to a risk factor and the underlying disease of interest and the prevalence of the risk factor, the attributable risk (AR) is probably the most commonly used epidemiologic measure for public health administrators to locate important risk factors. This paper discusses interval estimation of the AR in the presence of confounders under cross-sectional sampling. This paper considers four asymptotic interval estimators which are direct generalizations of those originally proposed for the case of no confounders, and employs Monte Carlo simulation to evaluate the finite-sample performance of these estimators in a variety of situations. This paper finds that interval estimators using Wald's test statistic and a quadratic equation suggested here can consistently perform reasonably well with respect to the coverage probability in all the situations considered here. This paper notes that the interval estimator using the logarithmic transformation, that is previously found to consistently perform well for the case of no confounders, may have the coverage probability less than the desired confidence level when the underlying common prevalence rate ratio (RR) across strata between the exposure and the non-exposure is large (≥4). This paper further notes that the interval estimator using the logit transformation is inappropriate for use when the underlying common RR =˙ 1. On the other hand, when the underlying common RR is large (≥4), this interval estimator is probably preferable to all the other three estimators. When the sample size is large (≥400) and the RR ≥ 2 in the situations considered here, this paper finds that all the four interval estimators developed here are essentially equivalent with respect to both the coverage probability and the average length.

15 citations

Book ChapterDOI
Paul Switzer1
01 Jan 1984
TL;DR: In this article, some general methods are proposed for generating confidence interval estimates for parameters of a variogram model, focusing on scale and shape parameters and joint estimation procedures for the scale parameter and the replication variance (nugget effect).
Abstract: Some general methods are proposed for generating confidence interval estimates for parameters of a variogram model. Particular attention is paid to scale and shape parameters and to joint estimation procedures for the scale parameter and the replication variance (nugget effect). The connection is made to formal testing of the hypothesis of the absence of spatial autocorrelation at a specified distance scale.

15 citations

Xu Cheng1
01 Jan 2008
TL;DR: In this paper, a local limit theory is proposed to provide a uniform approximation to the sample distribution irrespective of the strength of identi…cation of the unknown parameters within the nonlinear regression component.
Abstract: In this paper, we develop a practical procedure to construct con…dence intervals (CIs) in a weakly identi…ed nonlinear regression model. When the coe¢ cient of a nonlinear regressor is small, modelled here as local to zero, the signal from the respective nonlinear regressor is weak, resulting in weak identi…cation of the unknown parameters within the nonlinear regression component. In such cases, standard asymptotic theory can provide a poor approximation to …nite-sample behavior and failure to address the problem can produce misleading inferences. This paper seeks to tackle this problem in complementary ways. First, we develop a local limit theory that provides a uniform approximation to the …nite-sample distribution irrespective of the strength of identi…cation. Second, standard CIs based on conventional normal or chi-squared approximations as well as subsampling CIs are shown to be prone to size distortions that can be severe. Third, a new con…dence interval (CI) is constructed that has good …nite-sample coverage probability. Simulation results show that when the nonlinear function is a Box-Cox type transformation, the nominal 95% standard CI and subsampling CI have asymptotic sizes of 53% and 2.3%, respectively. In contrast, the robust CI has correct asymptotic size and a …nite-sample coverage probability of 93.4% when sample size is 100.

15 citations

Journal ArticleDOI
TL;DR: In this article, the authors present confidence intervals that are correct when conditioning on the subset of data for which a trial stopped at a particular analysis, and then use conditional coverage probabilities to compare the sample mean, stagewise, and repeated confidence intervals.
Abstract: The work of Fisher (1959) and Buehler (1959) discuss the importance of conditioning on recognizable subsets of the sample space. The stopping time yields an easily identifiable partition of the sample space when considering group sequential testing. We first present confidence intervals that are correct when conditioning on the subset of data for which a trial stopped at a particular analysis. These intervals have very desirable properties for observations that are highly unusual (given any value of the mean). In addition, they provide insight into how information about the mean is distributed between the two sufficient statistics. We then use conditional coverage probabilities to compare the sample mean, stagewise, and repeated confidence intervals. We find that none of these intervals outperforms the others when conditioning on stopping time, and no interval is a uniformly acceptable performer.

15 citations

Journal ArticleDOI
TL;DR: In this article, a parametric bootstrap method is proposed for constructing simultaneous confidence intervals (SCIs) for all pairwise differences of means from several two-parameter exponential distributions.

15 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
202363
2022153
2021142
2020151
2019142