Rui Song
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Rui Song has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chaeryon Kang
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, USA
1 shared publication - Eric B. Laber
Department of Statistical Science Duke University
1 shared publication - Hunyong Cho
University of North Carolina at Chapel Hill, Chapel Hill, USA
1 shared publication - Michael R. Kosorok
Department of Biostatistics University of North Carolina at Chapel Hill, USA
1 shared publication - Moulinath Banerjee
University of Michigan, Ann Arbor, USA
1 shared publication
Latest Publications
- Open Access
- 1 August 2025
- Published by Bernoulli Society for Mathematical Statistics and Probability in Bernoulli
- Vol. 31 (3) , 2459-2483
Inference for change-plane regression
A key challenge in analyzing the behavior of change-plane estimators is that the objective function has multiple minimizers. Two estimators are proposed to deal with this non-uniqueness. For each estimator, an n-rate of convergence is established, and the limiting distribution is derived. Based on these results, we provide a parametric bootstrap procedure for inference. The validity of our theoretical results and the finite sample performance of the bootstrap are demonstrated through simulation experiments. We demonstrate the proposed methods by applying them to latent subgroup identification in precision medicine using the ACTG175 AIDS study data.
- 1 August 2025
- Published by Bernoulli Society for Mathematical Statistics and Probability in Bernoulli
- Vol. 31 (3) , 2459-2483
Open AccessInference for change-plane regression
A key challenge in analyzing the behavior of change-plane estimators is that the objective function has multiple minimizers. Two estimators are proposed to deal with this non-uniqueness. For each estimator, an n-rate of convergence is established, and the limiting distribution is derived. Based on these results, we provide a parametric bootstrap procedure for inference. The validity of our theoretical results and the finite sample performance of the bootstrap are demonstrated through simulation experiments. We demonstrate the proposed methods by applying them to latent subgroup identification in precision medicine using the ACTG175 AIDS study data.