scholarly journals Bootstrap Based Confidence Interval Estimation of \\[6pt]Quantiles for Current Status Data

2021 ◽  
Vol 10 (5) ◽  
pp. 38
Author(s):  
Wei Chen ◽  
Fengling Ren
Keyword(s):  
Confidence Interval ◽  
Interval Estimation ◽  
Real Data ◽  
Current Status Data ◽  
Current Status ◽  
Well Performance ◽  
Data Set ◽  
Bootstrap Approach ◽  
Confidence Interval Estimation ◽  
Status Data

In this paper, we proposed a bootstrap approach to construct the confidence interval of quantiles for current status data, which is computationally simple and efficient without estimating nuisance parameters. The reasonability of the proposed method is verified by the well performance presented in the extensive simulation study. We also analyzed a real data set as illustration.

scholarly journals Confidence Interval Estimation of an ROC Curve: An Application of Generalized Half Normal and Weibull Distributions

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
S. Balaswamy ◽  
R. Vishnu Vardhan
Keyword(s):  
Roc Analysis ◽  
Interval Estimation ◽  
Area Under The Curve ◽  
Real Data ◽  
Simulation Studies ◽  
Recent Past ◽  
Data Set ◽  
Generalized Distributions ◽  
Accuracy Measure ◽  
Confidence Interval Estimation

In the recent past, the work in the area of ROC analysis gained attention in explaining the accuracy of a test and identification of the optimal threshold. Such types of ROC models are referred to as bidistributional ROC models, for example Binormal, Bi-Exponential, Bi-Logistic and so forth. However, in practical situations, we come across data which are skewed in nature with extended tails. Then to address this issue, the accuracy of a test is to be explained by involving the scale and shape parameters. Hence, the present paper focuses on proposing an ROC model which takes into account two generalized distributions which helps in explaining the accuracy of a test. Further, confidence intervals are constructed for the proposed curve; that is, coordinates of the curve (FPR, TPR) and accuracy measure, Area Under the Curve (AUC), which helps in explaining the variability of the curve and provides the sensitivity at a particular value of specificity and vice versa. The proposed methodology is supported by a real data set and simulation studies.

Mortality rate and confidence interval estimation in humanitarian emergencies

Disasters ◽  
2009 ◽  
Vol 34 (1) ◽  
pp. 164-175 ◽  
Author(s):  
Kevin Sullivan ◽  
S.M. Moazzem Hossain ◽  
Bradley A. Woodruff
Keyword(s):  
Mortality Rate ◽  
Confidence Interval ◽  
Interval Estimation ◽  
Humanitarian Emergencies ◽  
Confidence Interval Estimation
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