R
Runze Li
Researcher at Pennsylvania State University
Publications - 304
Citations - 25154
Runze Li is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Estimator & Feature selection. The author has an hindex of 53, co-authored 272 publications receiving 21336 citations. Previous affiliations of Runze Li include Academia Sinica & Penn State Cancer Institute.
Papers
More filters
Journal ArticleDOI
Feature Screening in Ultrahigh Dimensional Cox's Model.
TL;DR: Wang et al. as discussed by the authors proposed a feature screening procedure for the Cox model with ultra-high dimensional covariates, which can effectively identify active predictors that are jointly dependent but marginally independent of the response without performing an iterative procedure.
Book ChapterDOI
Empirical Likelihood in Survival Analysis
TL;DR: In this paper, an overview of recent developments of the empirical likelihood for survival data is presented, focusing on two regression models: the Cox proportional hazards model and the accelerated failure time model.
Journal ArticleDOI
Testing a single regression coefficient in high dimensional linear models
TL;DR: The Correlated Predictors Screening (CPS) method is introduced by introducing the z-test to assess the significance of each covariate and it is shown that the multiple hypothesis testing achieves consistent model selection.
Journal ArticleDOI
Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation.
TL;DR: A new estimation and valid inference method for single or low-dimensional regression coefficients in high-dimensional generalized linear models and it is proved the proposed CI is asymptotically narrower than the CIs constructed based on the desparsified Lasso estimator and the decorrelated score statistic.
Journal ArticleDOI
On the Feasibility of Distributed Kernel Regression for Big Data
TL;DR: It is shown that, with proper data segmentation, DKR leads to an estimator that is generalization consistent to the unknown regression function, which theoretically justifies DKR and sheds light on more advanced distributive algorithms for processing Big Data.