基于密度比模型的pAUC半参数估计方法及其应用
A semi-parametric estimation method for pAUC based on the density ratio model and its application
余昊 1赵超群 1杨建萍2
作者信息
- 1. 浙江理工大学计算机科学与技术学院,杭州 310018
- 2. 浙江理工大学理学院,杭州 310018
- 折叠
摘要
为了进一步提高pAUC(Partial area under curve)估计精度和医学诊断测试精确性,提出了一种基于密度比模型的pAUC半参数估计方法,并从理论和仿真两个方面研究其性质.首先,根据密度比模型,用半参数极大似然估计方法得到了 pAUC半参数估计量,并用大样本理论分析了它的统计性能;然后,对pAUC半参数估计方法在实际应用中的性能进行了仿真,并与现有精度较高的pAUC非参数估计方法进行比较.研究发现,pAUC半参数估计量不仅具有相合渐近正态性等重要的统计性质,而且比已有的非参数pAUC估计量具有更高的渐近估计效率和精确度.将该pAUC半参数估计方法应用于乳腺癌诊断模型的筛选,得到了一个预测精度更高的新乳腺癌诊断模型,结果表明该方法在实际应用中能提高医学诊断测试的精度.
Abstract
In order to further improve the estimation accuracy of pAUC(partial area under curve)and the accuracy of medical diagnosis tests,a semi-parameter estimation method of pAUC based on density ratio model is proposed,and its properties are studied from both theoretical and simulation aspects.Firstly,according to the density ratio model,the semi-parametric maximum likelihood estimator of pAUC is obtained based on the semi-parametric maximum likelihood estimation method,and its statistical performance is analyzed by using the large sample theory.Then,the performance of the pAUC semi-parametric estimation method in practical application is simulated and compared with the existing non-parametric estimation method in term of accuracy.It is found that not only the semi-parametric pAUC estimator has important statistical properties such as consistent asymptotic normality,but also it has higher asymptotic estimation efficiency and accuracy than the existing nonparametric pAUC estimator.The semi-parameter estimation method for pAUC is applied to the screening of breast cancer diagnosis models,and a new breast cancer diagnosis model with higher prediction accuracy is obtained.The result shows that the proposed method can improve the accuracy of medical diagnosis tests in practical applications.
关键词
pAUC/半参数估计/密度比模型/渐近正态性/医学诊断Key words
pAUC/semi-parametric estimatior/density ratio model/asymptotic normality/medical diagnosis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据