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Smoothed empirical likelihood inference for ROC curve in the presence of missing biomarker values
Author(s) -
Cheng Weili,
Tang Niansheng
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201900121
Subject(s) - imputation (statistics) , empirical likelihood , receiver operating characteristic , estimator , missing data , inference , nonparametric statistics , mathematics , statistics , computer science , econometrics , artificial intelligence
This paper considers statistical inference for the receiver operating characteristic (ROC) curve in the presence of missing biomarker values by utilizing estimating equations (EEs) together with smoothed empirical likelihood (SEL). Three approaches are developed to estimate ROC curve and construct its SEL‐based confidence intervals based on the kernel‐assisted EE imputation, multiple imputation, and hybrid imputation combining the inverse probability weighted imputation and multiple imputation. Under some regularity conditions, we show asymptotic properties of the proposed maximum SEL estimators for ROC curve. Simulation studies are conducted to investigate the performance of the proposed SEL approaches. An example is illustrated by the proposed methodologies. Empirical results show that the hybrid imputation method behaves better than the kernel‐assisted and multiple imputation methods, and the proposed three SEL methods outperform existing nonparametric method.