A Semi-parametric Approach to Estimation of ROC Curves for Multivariate Binormal Mixtures
نویسندگان
چکیده
A Receiver Operating Characteristic (ROC) curve reflects the performance of a system which decides between two competing actions in a test of statistical hypothesis. This paper addresses the inference on ROC curves for the following problem: how can one statistically validate the performance of a system with a claimed ROC curve, ROC0 say? Our proposed solution consists of two main components: First, a flexible family of distributions, namely the multivariate binormal mixtures, is proposed to account for intra-sample correlation and non-Gaussianity of the marginal distributions under both the null and alternative hypotheses. Second, a semiparametric inferential framework is developed for estimating all unknown parameters based on a rank likelihood. Actual inference is carried out by running a Gibbs sampler until convergence, and subsequently constructing a highest posterior density (HPD) set for the true but unknown ROC curve based on the Gibbs output. Real data are analyzed to support out theoretical results.
منابع مشابه
Mixtures of receiver operating characteristic curves.
RATIONALE AND OBJECTIVES Receiver operating characteristic (ROC) curves are ubiquitous in the analysis of imaging metrics as markers of both diagnosis and prognosis. While empirical estimation of ROC curves remains the most popular method, there are several reasons to consider smooth estimates based on a parametric model. MATERIALS AND METHODS A mixture model is considered for modeling the di...
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