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: The Buehler 1−α upper confidence limit is as small as possible, subject to the constraints that its coverage probability never falls below 1 −α and that it is a non-decreasing function of a designated statistic T as discussed by the authors.
13 citations
TL;DR: The construction of confidence sets when multivariate normality holds and in the general case where the usual spherical or elliptical structures may not occur is investigated in this article, where calibration is used to correct the coverage probability of the nonparametric sets.
Abstract: The construction of confidence sets when multivariate normality holds and in the general case where the usual spherical or elliptical structures may not occur is investigated. Calibration is used to correct the coverage probability of the nonparametric sets, and an example involving parameters from a chemical kinetics model in a biological system is used to demonstrate the techniques. Monte Carlo simulations validate the approach.
13 citations
TL;DR: It is verified that the proposed FSRC scheme achieves a maximum of approximately 37% and 33% improvement of the minimum success probability and coverage probability, respectively, under practical LoRa PHY/MAC parameters, compared to the single-hop environment (without relay operation).
Abstract: This article proposes a novel fair and scalable relay control (FSRC) scheme for the Internet-of-Things (IoT) services in long range (LoRa)-based low-power wide-area networks. The proposed FSRC scheme promotes relay operation with low spreading factor (SF) to improve the success probability for distant end-devices (EDs) and the fairness of success probability for each SF region. To achieve this, a theoretical framework for designing the relay operation is analytically developed by considering a practical LoRaWAN MAC protocol as an analytical model. The proposed FSRC scheme encompasses a selective relay operation by considering both signal-to-noise ratio and signal-to-interference ratio and the receive signal strength indicator value for the location-unaware relay selection strategy. Using this model, a genetic algorithm-based relay control strategy is proposed to maximize both coverage probability and minimum success probability for all SF regions by controlling the relay parameters, such as source-relay region and source-relay ratio. Our numerical analysis validates the effectiveness of the proposed FSRC scheme under various parameters in terms of the minimum success probability of each SF region, coverage probability, and fairness. Specifically, we verify that the proposed FSRC scheme achieves a maximum of approximately 37% and 33% improvement of the minimum success probability and coverage probability, respectively, under practical LoRa PHY/MAC parameters, compared to the single-hop environment (without relay operation).
13 citations
TL;DR: A generalized confidence interval for the slope parameter in linear measurement error model is proposed in this article, which is based on the relation between the slope of classical regression model and the measurement errors model.
Abstract: A generalized confidence interval for the slope parameter in linear measurement error model is proposed in this article, which is based on the relation between the slope of classical regression model and the measurement error model. The performance of the confidence interval estimation procedure is studied numerically through Monte Carlo simulation in terms of coverage probability and expected length.
13 citations
TL;DR: It is recommended that MMRM-UN analysis using the Kenward-Roger method based on a common covariance matrix for treatment groups is not recommended, although it is frequently seen in applications, when heteroscedasticity between the groups is apparent in incomplete longitudinal data.
Abstract: Mixed-effects models for repeated measures (MMRM) analyses using the Kenward-Roger method for adjusting standard errors and degrees of freedom in an "unstructured" (UN) covariance structure are increasingly becoming common in primary analyses for group comparisons in longitudinal clinical trials. We evaluate the performance of an MMRM-UN analysis using the Kenward-Roger method when the variance of outcome between treatment groups is unequal. In addition, we provide alternative approaches for valid inferences in the MMRM analysis framework. Two simulations are conducted in cases with (1) unequal variance but equal correlation between the treatment groups and (2) unequal variance and unequal correlation between the groups. Our results in the first simulation indicate that MMRM-UN analysis using the Kenward-Roger method based on a common covariance matrix for the groups yields notably poor coverage probability (CP) with confidence intervals for the treatment effect when both the variance and the sample size between the groups are disparate. In addition, even when the randomization ratio is 1:1, the CP will fall seriously below the nominal confidence level if a treatment group with a large dropout proportion has a larger variance. Mixed-effects models for repeated measures analysis with the Mancl and DeRouen covariance estimator shows relatively better performance than the traditional MMRM-UN analysis method. In the second simulation, the traditional MMRM-UN analysis leads to bias of the treatment effect and yields notably poor CP. Mixed-effects models for repeated measures analysis fitting separate UN covariance structures for each group provides an unbiased estimate of the treatment effect and an acceptable CP. We do not recommend MMRM-UN analysis using the Kenward-Roger method based on a common covariance matrix for treatment groups, although it is frequently seen in applications, when heteroscedasticity between the groups is apparent in incomplete longitudinal data.
13 citations