On weak convergence of quantile-based empirical likelihood process for ROC curves

IF 1.6 2区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Statistics and Computing Pub Date : 2024-07-04 DOI:10.1007/s11222-024-10457-x

Hu Jiang, Liu Yiming, Zhou Wang

引用次数: 0

Abstract

The empirical likelihood (EL) method possesses desirable qualities such as automatically determining confidence regions and circumventing the need for variance estimation. As an extension, a quantile-based EL (QEL) method is considered, which results in a simpler form. In this paper, we explore the framework of the QEL method. Firstly, we explore the weak convergence of the −2 log empirical likelihood ratio for ROC curves. We also introduce a novel statistic for testing the entire ROC curve and the equality of two distributions. To validate our approach, we conduct simulation studies and analyze real data from hepatitis C patients, comparing our method with existing ones.

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论 ROC 曲线基于量化的经验似然过程的弱收敛性

经验似然法（EL）具有自动确定置信区域和无需方差估计等优点。作为扩展，我们考虑了一种基于量值的 EL（QEL）方法，它的形式更为简单。本文将探讨 QEL 方法的框架。首先，我们探讨了 ROC 曲线的-2 对数经验似然比的弱收敛性。我们还引入了一种新的统计量，用于测试整个 ROC 曲线和两个分布的相等性。为了验证我们的方法，我们进行了模拟研究，并分析了丙型肝炎患者的真实数据，将我们的方法与现有方法进行了比较。

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来源期刊

Statistics and Computing 数学-计算机：理论方法

CiteScore

3.20

自引率

4.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.

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