5 citations · 8 across the 4 of their papers we have counts for
6 papers
Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data
Shanpeng Li, Ning Li, Hong Wang +3
Semiparametric joint models of longitudinal and competing risks data are computationally costly and their current implementations do not scale well to massive biobank data. This pa…
A Flexible Joint Model for Multiple Longitudinal Biomarkers and A Time-to-Event Outcome: With Applications to Dynamic Prediction Using Highly Correlated Biomarkers
Ning Li, Yi Liu, Shanpeng Li +2
In biomedical studies it is common to collect data on multiple biomarkers during study follow-up for dynamic prediction of a time-to-event clinical outcome. The biomarkers are typi…
Scalable Algorithms for Large Competing Risks Data
Eric S. Kawaguchi, Jenny I. Shen, Marc A. Suchard +1
This paper develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge meth…
A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk
Eric S Kawaguchi, Jenny I Shen, Gang Li +1
Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data…
An Oracle Property of The Nadaraya-Watson Kernel Estimator for High Dimensional Nonparametric Regression
Daniel Conn, Gang Li
The celebrated Nadaraya-Watson kernel estimator is among the most studied method for nonparametric regression. A classical result is that its rate of convergence depends on the num…
Prediction Accuracy Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data
Gang Li, Xiaoyan Wang
This paper studies prediction summary measures for a prediction function under a general setting in which the model is allowed to be misspecified and the prediction function is not…