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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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TL;DR: In this article, a conditional bias correction method was proposed to correct the deficiencies in the naive EB intervals. But this method cannot account for the variability in the estimation of the hyperparameters, resulting in sub-nominal coverage probability in the EB sense defined in Morris (1983a).
Abstract: : Parametric empirical Bayes methods of point estimation for a vector of unknown parameters date to the landmark paper of James and Stein (1961). The usual approach is to use the mean of the estimated posterior distribution of each parameter, where the estimation of the prior parameters (hyperparameters) is accomplished through the marginal distribution of the data. While point estimates computed this way usually perform well, interval estimates based on the estimated posterior (called naive EB intervals) are not. They fail to account for the variability in the estimation of the hyperparameters, generally resulting in sub-nominal coverage probability in the EB sense defined in Morris (1983a). In this paper we extend the work of Carlin and Gelfand (1989), who proposed a conditional bias correction method for developing EB intervals which corrects the deficiencies in the naive intervals. We show how bias correction can be implemented in general via a Type III parametric bootstrap procedure, a sample reuse method first employed by Laird and Louis (1987).
49 citations
TL;DR: A thorough comparison of new and established interval estimators for p, the probability of disease transmissi on by a single vector, in terms of coverage probability and mean length is provided.
Abstract: In plant pathology, group testing has been widely used in vector-transfer designs to study factors affecting the spread of disease by insect vectors. In such contexts, vectors are tested in groups rather than individually. However, the goal is still to estimate p, the probability of disease transmissi on by a single vector. The purpose of this article is to provide a thorough comparison of new and established interval estimators for p in terms of coverage probability and mean length. We ill ustrate our methods using data from an Argentinean study in volving the Mal Rio Cuarto virus and its transmission by the Delphacodes kuscheli planthopper.
48 citations
TL;DR: In this article, the authors compare the empirical coverage probability of confidence intervals based on both the standard normal distribution and the t-distribution, in conjunction with several methods of estimating the heterogeneity variance for a standardized mean difference.
Abstract: Under the random effects model for meta-analysis, confidence intervals for the overall effect are typically constructed using quantiles of the standard normal distribution. We discuss confidence intervals based on both the standard normal distribution and the t-distribution, in conjunction with several methods of estimating the heterogeneity variance for a standardized mean difference, and we compare the empirical coverage probabilities of the intervals using simulation. The coverage probabilities of intervals based on an approximate t-statistic are higher than the coverage probabilities for the standard normal intervals, and are very close to the specified confidence level even for small meta-analysis sample size. Moreover, intervals based on the approximate t-statistic appear relatively robust to different methods of estimating the heterogeneity variance, unlike the normal intervals. Thus, we conclude that confidence intervals based on the t-statistic are superior to the standard normal confide...
48 citations
TL;DR: In this paper, the authors developed a SAS macro to construct two-sided confidence intervals for multinomial proportions that is based on the doubly truncated Poisson distribution and their method performs well when the cell counts are fairly equally dispersed over a large number of categories.
Abstract: Confidence intervals for multinomial proportions are often constructed using large-sample methods that rely on expected cell counts of 5 or greater. In situations that give rise to a large number of categories, the cell counts may not be of adequate size to ensure the appropriate overall coverage probability and alternative methods of construction have been proposed. Sison and Glaz (1995) developed a method of constructing two-sided confidence intervals for multinomial proportions that is based on the doubly truncated Poisson distribution and their method performs well when the cell counts are fairly equally dispersed over a large number of categories. In fact, the Sison and Glaz (1995) intervals appear to outperform other methods of simultaneous construction in terms of coverage probabilities and interval length in these situations. To make the method available to researchers, we have developed a SAS macro to construct the intervals proposed by Sison and Glaz (1995).
48 citations
TL;DR: In simulations for a variety of first-order autoregressions, it is found that calibration gives reasonably narrow prediction intervals with the lowest coverage probability mean squared error among the methods used.
Abstract: We consider bootstrap construction and calibration of prediction intervals for nonGaussian autoregressions In particular, we address the question of prediction conditioned on the last p observations of the process, for which we offer an exact simulation technique and an approximate bootstrap approach In simulations for a variety of first-order autoregressions, we compare various nonparametric prediction intervals and find that calibration gives reasonably narrow prediction intervals with the lowest coverage probability mean squared error among the methods used
47 citations