5 papers
Semiparametric Receiver Operating Characteristic Analysis in the Presence of an Imperfect Reference Standard via a Box-Cox Density Ratio Model
Yi Chang, Siyan Liu, Qinglong Tian +1
Receiver operating characteristic (ROC) analysis is commonly used to evaluate the diagnostic accuracy of continuous biomarkers. In practice, the true disease status may be unavaila…
In-Context Positive-Unlabeled Learning
Siyan Liu, Yi Chang, Manli Cheng +2
Positive-unlabeled (PU) learning addresses binary classification when only a set of labeled positives is available alongside a pool of unlabeled samples drawn from a mixture of pos…
Semiparametric Joint Inference for Sensitivity and Specificity at the Youden-Optimal Cut-Off
Siyan Liu, Qinglong Tian, Chunlin Wang +1
Sensitivity and specificity evaluated at an optimal diagnostic cut-off are fundamental measures of classification accuracy when continuous biomarkers are used for disease diagnosis…
Semiparametric Learning from Open-Set Label Shift Data
Siyan Liu, Yukun Liu, Qinglong Tian +2
We study the open-set label shift problem, where the test data may include a novel class absent from training. This setting is challenging because both the class proportions and th…
Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
Siyan Liu, Chi-Kuang Yeh, Xin Zhang +2
This study introduces a new approach to addressing positive and unlabeled (PU) data through the double exponential tilting model (DETM). Traditional methods often fall short becaus…