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: A variant of plug-in empirical likelihood is applied by estimating the cumulative baseline hazard function of the Cox’s regression model to evaluate the performance of the proposed AEL method by comparing it with normal approximation (NA) based method.
Abstract: The Cox’s regression model is one of the most popular tools used in survival analysis. Recently, Qin and Jing (Commun Stat Simul Comput 30:79–90, 2001) applied empirical likelihood to study it with the assumption that baseline hazard function is known. However, in the Cox’s regression model the baseline hazard function is unspecified. Thus, their method suffers from severe defect. In this paper, we apply a variant of plug-in empirical likelihood by estimating the cumulative baseline hazard function. Adjusted empirical likelihood (AEL) confidence regions for the vector of regression parameters are obtained. Furthermore, we conduct a simulation study to evaluate the performance of the proposed AEL method by comparing it with normal approximation (NA) based method. The simulation studies show that both methods produce comparable coverage probabilities. The proposed AEL method outperforms the NA method based on power analysis.
15 citations
TL;DR: In this article, a score test of hypotheses pertaining to the marginal and conditional probabilities in a 2 × 2 table with structural zero via the risk/rate difference measure was proposed, and the performance of the score test and the existing likelihood ratio test was evaluated.
Abstract: In some infectious disease studies and 2-step treatment studies, 2 × 2 table with structural zero could arise in situations where it is theoretically impossible for a particular cell to contain observations or structural void is introduced by design. In this article, we propose a score test of hypotheses pertaining to the marginal and conditional probabilities in a 2 × 2 table with structural zero via the risk/rate difference measure. Score test-based confidence interval will also be outlined. We evaluate the performance of the score test and the existing likelihood ratio test. Our empirical results evince the similar and satisfactory performance of the two tests (with appropriate adjustments) in terms of coverage probability and expected interval width. Both tests consistently perform well from small- to moderate-sample designs. The score test however has the advantage that it is only undefined in one scenario while the likelihood ratio test can be undefined in many scenarios. We illustrate our method by a real example from a two-step tuberculosis skin test study.
15 citations
TL;DR: In this article, the authors derive an analytic expression for the n − 1 term, which may be used to calibrate the nominal coverage level to get O ( n − 3 / 2 [ log ( n ) ] 3 ) coverage error.
15 citations
TL;DR: In this paper, the results of an extensive simulation study using both real and simulated data comparing 14 confidence bounds were presented, and no one method was found to be superior in terms of reliability and efficiency.
Abstract: Auditors typically employ one-sided confidence bounds to estimate the total error in an audit population. This estimate provides an auditor with a given level of assurance that the total error does not exceed the upper confidence bound. This paper summarizes the results of an extensive simulation study using both real and simulated data comparing 14 bounds. No one method was found to be superior in terms of reliability and efficiency. A 95% upper bound is reliable if, when used repeatedly, the bound exceeds the true audit error 95% of the time. Efficiency measures the size of the bound; the smaller the bound is, the more efficient it is said to be. The multinomial-Dirichlet method [Tsui, K. W., Matsamura, E. M., Tsui, K. L. (1985). Multinomial-Dirichlet bounds for dollar-unit sampling in auditing. Acc. Rev. 60(1):76–96] demonstrated the best reliability for a variety of populations. The Bayesian normal bound [Menzefricke, U., Smieliauskas, W. (1984). A simulation study of the performance of param...
15 citations
TL;DR: In this article, the authors investigate how much the logarithmic transformation improves the approximation of the distribution of a sample proportion to a normal distribution, and they find that the success of a normal approximation has less to do with the size of the event rates than the values of np.
Abstract: Decisions about how to best analyze rare events need to be made in many investigations. For binary events, a normal approximation is often said to be satisfactory when the expected number of events is larger than 5 and the binomial proportion is not too close to zero or one. In most empirical research, the commonly employed large-sample method for determining a confidence interval or for testing a hypothesis for a parameter is based on its logarithmic transformed estimator. In this article, we investigate how much the logarithmic transformation improves the approximation of the distribution of a sample proportion to a normal distribution. We also investigate the performance of the arcsine square root transformation. We find that the success of a normal approximation has less to do with the size of the event rates than the values of np. Further, we find that the transformations do not substantially improve the normal approximation of the distribution of a sample proportion in computing coverage probabiliti...
15 citations