Evaluation of Area under the Constant Shape Bi-Weibull ROC Curve

The Receiver Operating Characteristic (ROC) curve generated based on assuming a constant shape Bi-Weibull distribution is studied. In the context of ROC curve analysis, it is assumed that biomarker values from controls and cases follow some specific distribution and the accuracy is evaluated by using the ROC model developed from that specified distribution. This article assumes that the biomarker values from the two groups follow Weibull distributions with equal shape parameter and different scale parameters. The ROC model, area under the ROC curve (AUC), asymptotic and bootstrap confidence intervals for the AUC are derived. Theoretical results are validated by simulation studies

Evaluation of biomarker using two parameter bi-exponential ROC curve

Receiver Operating Characteristic (ROC) Curve is used for assessing the ability of a biomarker/screening test to discriminate between non-diseased and diseased subject. In this paper, the parametric ROC curve is studied by assuming two-parameter exponential distribution to the biomarker values. The ROC model developed under this assumption is called bi-exponential ROC (EROC) model. Here, the research interest is to know how far the biomarker will make a distinction between diseased and non-diseased subjects when the gold standard is available using parametric EROC curve and its Area Under the EROC Curve (AUC).  Here, the standard error is used as an estimate of the precision of the accuracy measure AUC. The properties of EROC curve that explains the behavior of the EROC curve are also discussed. The AUC along with its asymptotic variance and confidence interval are derived. 

Evaluation of Diagnostic Accuracy and its Standard Error using Constant Shape Weibull Mixture ROC Curve

Receiver Operating Characteristic (ROC) Curve is a widely used classification technique in Medical Diagnosis which classifies the healthy and diseased individuals on the basis of optimal cut off value of the biomarker. In this article, we have proposed Constant Shape Weibull Mixture ROC (CSWMROC) model. The properties of CSWMROC Curve are discussed and expressions for AUC, its variance and confidence interval are derived. The estimates of AUC of CSWMROC curve are obtained using Method of Moments (MOM). Numerical example is considered to support the proposed theory

Intervened Exponential Distribution: Properties and Applications

This manuscript aims to study the intervention-based probability model. Statistical and reliability properties such as the expressions for, cumulative density function (CDF), mean deviations about mean and median, rth order central and non-central moments, â€generation functions†for moments have been derived. Moreover, the expression for reliability function, hazard rate, reverse hazard rate, aging intensity, mean residual life function, stress-strength reliability, and entropy metrics due to R´enyi and Shannon are also derived. Monte Carlo simulation study performance of maximum likelihood estimates (MLEs) has been carried out, followed by calculations of Average Bias (ABias), and Mean Square Error (MSE). The applicability of the model in real-life situations has been discussed by analyzing the two real-life data sets

Bonferroni Curve and the related statistical inference

Two measures of inequality, viz. the Bonferroni Curve and the Bonferroni index are studied. It is shown that these have some relationship with Lorenz Curve, Gini ratio and certain concepts used in reliability, life testing and renewal theory. The problem of testing of Bonferroni Curve and its ordinate(s) is discussed in the multivariate set-up. The variance-covariance structure of the vector of Bonferroni Curve ordinates is derived and a simulation exercise is done.Bonferroni Curve Bonferroni index Lorenz Curve Gini index Cumulative total time on test transform Mean residual life-time Asymptotic distribution Renewal theory