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 newly proposed PI estimation method called Lower Upper Bound Estimation (LUBE) method, which adopts an Artificial Neural Network with two outputs to directly generate the upper and lower bounds of PI without making any assumption about the data distribution is extended.
Abstract: It is widely accepted that Prediction Interval (PI) can provide more accurate and precise information than deterministic forecast when the uncertainty level increases in flood forecasting. Coverage probability and PI width are two main criteria used to assess the constructed PI, rarely has there been an index to quantify the symmetry between target value and PI. This study extends a newly proposed PI estimation method called Lower Upper Bound Estimation (LUBE) method, which adopts an Artificial Neural Network (ANN) with two outputs to directly generate the upper and lower bounds of PI without making any assumption about the data distribution. A new Prediction Interval Symmetry (PIS) index is introduced and a new objective function is developed for the comprehensive evaluation of PI considering their coverage probability, width and symmetry. Furthermore, Shuffled Complex Evolution algorithm (SCE-UA) is used to minimize the objective function and optimize ANN parameters in the LUBE method. The proposed method is applied to a real world flood forecasting case study of the upper Yangtze River Watershed. The result shows that the SCE-UA based LUBE method with new objective function is very efficient, meanwhile, the midpoint forecasting of the PI obtains excellent performance by evidently improving the symmetry of PI.
22 citations
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TL;DR: In this article, a new method is proposed for constructing confidence intervals on the response variance in the unbalanced case of the one-way variance component model via generalized inference, which can be derived by the fiducial method directly and easily.
Abstract: In this article, a new method is proposed for constructing confidence intervals on the response variance in the unbalanced case of the one-way variance component model via generalized inference. It is shown that the generalized pivotal quantity in the method can be derived by the fiducial method directly and easily. To compare the resulted interval with the Modified Large Sample (MLS) interval by Burdick and Graybill (1984) and an approximate generalized confidence interval, a simulation study is conducted. The results indicate that the proposed method performs better than the other two methods, especially for very unbalanced designs.
22 citations
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TL;DR: It is shown that in small to moderately sized samples, these standard error estimates can be severely biased downward, therefore inflating test size and decreasing coverage probability, and this work proposes adjustments to the asymptotic covariance formula that eliminate finite-sample biases and lead to substantial improvements in standard errors, inference, and coverage.
Abstract: The method of quadratic inference functions (QIF) is an increasingly popular method for the analysis of correlated data because of its multiple advantages over generalized estimating equations (GEE). One advantage is that it is more efficient for parameter estimation when the working covariance structure for the data is misspecified. In the QIF literature, the asymptotic covariance formula is used to obtain standard errors. We show that in small to moderately sized samples, these standard error estimates can be severely biased downward, therefore inflating test size and decreasing coverage probability. We propose adjustments to the asymptotic covariance formula that eliminate finite-sample biases and, as shown via simulation, lead to substantial improvements in standard error estimates, inference, and coverage. The proposed method is illustrated in application to a cluster randomized trial and a longitudinal study. Furthermore, QIF and GEE are contrasted via simulation and these applications.
22 citations
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TL;DR: In this paper, an objective Bayesian method was proposed to analyze the accelerated degradation model based on the inverse Gaussian process, and the propriety of the posteriors under each prior was validated.
22 citations
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TL;DR: In this paper, a Bayesian method of determining credible intervals for response surface optima was developed, which is readily applicable to the kind of constrained and/or nonlinear problems that frequently appear in practice.
22 citations