Topic
Coverage probability
About: Coverage probability is a research topic. Over the lifetime, 2479 publications have been published within this topic receiving 53259 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: Heterogeneity estimators are identified that perform better than the suggested Paule-Mandel estimator and maximum likelihood provides the best performance for both types of outcome in the absence of heterogeneity.
Abstract: When we synthesize research findings via meta-analysis, it is common to assume that the true underlying effect differs across studies. Total variability consists of the within-study and between-study variances (heterogeneity). There have been established measures, such as I2 , to quantify the proportion of the total variation attributed to heterogeneity. There is a plethora of estimation methods available for estimating heterogeneity. The widely used DerSimonian and Laird estimation method has been challenged, but knowledge of the overall performance of heterogeneity estimators is incomplete. We identified 20 heterogeneity estimators in the literature and evaluated their performance in terms of mean absolute estimation error, coverage probability, and length of the confidence interval for the summary effect via a simulation study. Although previous simulation studies have suggested the Paule-Mandel estimator, it has not been compared with all the available estimators. For dichotomous outcomes, estimating heterogeneity through Markov chain Monte Carlo is a good choice if an informative prior distribution for heterogeneity is employed (eg, by published Cochrane reviews). Nonparametric bootstrap and positive DerSimonian and Laird perform well for all assessment criteria for both dichotomous and continuous outcomes. Hartung-Makambi estimator can be the best choice when the heterogeneity values are close to 0.07 for dichotomous outcomes and medium heterogeneity values (0.01 , 0.05) for continuous outcomes. Hence, there are heterogeneity estimators (nonparametric bootstrap DerSimonian and Laird and positive DerSimonian and Laird) that perform better than the suggested Paule-Mandel. Maximum likelihood provides the best performance for both types of outcome in the absence of heterogeneity.
41 citations
TL;DR: In this paper, the size distortions of tests for structural parameters in the simultaneous equations model were fixed by computing critical value functions based on the conditional distribution of test statistics, which can then be used to construct informative confidence regions for the structural parameter with correct coverage probability.
Abstract: This paper fixes size distortions of tests for structural parameters in the simultaneous equations model by computing critical value functions based on the conditional distribution of test statistics. The conditional tests can then be used to construct informative confidence regions for the structural parameter with correct coverage probability. Commands to implement these tests in Stata are also introduced.
41 citations
TL;DR: In this paper, distribution-free prediction intervals for individual records as well as outer and inner prediction intervals are derived based on X-records, and exact expressions for the coverage probabilities of these intervals are also derived.
41 citations
TL;DR: In this paper, confidence intervals of prescribed width for the lo-cation parameter of an exponential distribution are obtained for the coverage probability and expected sample size, from which an asymptotic expression for the variance of the sample size is deduced.
Abstract: We study confidence intervals of prescribed width for the lo-cation parameter of an exponential distribution. Asymptotic expan-sions up to terms tending to zero are obtained for the coverage probability and expected sample size. The limiting distribution of the sample size is given from which an asymptotic expression for the variance of the sample size is deduced. Sequential procedures with non-asymptotic coverage probability are also investigated
41 citations
TL;DR: In this article, a collection of parametric functions can be constructed in several different ways, including the exact pivotal method, asymptotic pivots, and bootstrapped roots.
Abstract: Simultaneous confidence sets for a collection of parametric functions may be constructed in several different ways. These ways include: (a) the exact pivotal method that underlies Tukey's (1953) and Scheffe's (1953) simultaneous confidence intervals for linear parametric functions in the normal linear model; (b) the method of asymptotic pivots, which is an approximate extension of the pivotal method; (c) the method of bootstrapped roots developed in Beran (1988). These three methods share several features. Each method simultaneously asserts a collection of confidence sets, one confidence set for every parametric function of interest. Each method obtains the uth constituent confidence set by referring a root to a critical value; the uth root is a function of the sample and of the uth parametric function. Each method has the same aim: to control the overall level of the simultaneous confidence set and to keep equal the marginal levels of the individual confidence statements that make up the simulta...
41 citations