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

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...

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Bibliographic Details
Published inStatistics and computing Vol. 34; no. 5
Main Authors Jiang, Hu, Yiming, Liu, Wang, Zhou
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2024
Springer Nature B.V
Subjects
Artificial Intelligence
Computer Science
Convergence
Likelihood ratio
Original Paper
Probability and Statistics in Computer Science
Quantiles
Statistical Theory and Methods
Statistics and Computing/Statistics Programs
Weak convergence
Quantile-based EL
ROC curves
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Summary: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.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-024-10457-x