1 citations · 1 across the 5 of their papers we have counts for
6 papers
Wilcoxon Random Forests for Robust Distributional Prediction
Danni Shi, Bryan E Shepherd, Chun Li
Random forests (RF) are tree-based models that capture complex, high-order predictor interactions. In biomedical studies, ordered outcomes often have outliers, skewness, or mixture…
Doubly Robust Estimators of Quantile Treatment Effects With Semiparametric Cumulative Probability Models
Hao Wu, Chun Li, Bryan E. Shepherd
The causal inference literature has traditionally focused on estimating the mean of the potential outcome, whereas evaluating how a treatment affects the entire outcome distributio…
Addressing errors in multiple variables using generalized raking and cumulative probability models
Eric S. Kawaguchi, Chun Li, Frank E. Harrell +3
Routinely collected data, such as electronic health record (EHR) data, are frequently used for biomedical research, but these data are prone to errors, which can bias study finding…
Probability-scale residuals for event-time data
Eric S. Kawaguchi, Bryan E. Shepherd, Chun Li
The probability-scale residual (PSR) is defined as , where is the observed outcome and is a random variable from the fitted distribution. The PSR is pa…
Between- and Within-Cluster Spearman Rank Correlations
Shengxin Tu, Chun Li, Bryan E. Shepherd
Clustered data are common in practice. Clustering arises when subjects are measured repeatedly, or subjects are nested in groups (e.g., households, schools). It is often of interes…
Addressing Detection Limits with Semiparametric Cumulative Probability Models
Yuqi Tian, Chun Li, Shengxin Tu +3
Detection limits (DLs), where a variable is unable to be measured outside of a certain range, are common in research. Most approaches to handle DLs in the response variable implici…