Forthcoming Issue

Forthcoming Issue Papers to appear in Volume 35, No. 2, January 2025 (subject to change) Corresponding author*
General
1. SS-2021-0389 [Supp]	doi:10.5705/ss.202021.0389
Partially-Global Fréchet Regression Danielle C. Tucker and Yichao Wu*
2. SS-2022-0384 [Supp]	doi:10.5705/ss.202022.0384
Moment Deviation Subspaces of Dimension Reduction for High-Dimensional Data With Change Structure Xuehu Zhu, Luoyao Yu, Jiaqi Huang, Junmin Liu and Lixing Zhu*
3. SS-2022-0371	doi:10.5705/ss.202022.0371
Improved Regression Inference Using a Second Overlapping Regression Model Liang Peng and John Einmahl*
4. SS-2021-0387 [Supp]	doi:10.5705/ss.202021.0387
Statistical Inference With Anchored Bayesian Mixture of Regressions Models: An Illustrative Study of Allometric Data Deborah Kunkel* and Mario Peruggia
5. SS-2022-0351 [Supp]	doi:10.5705/ss.202022.0351
Robust Recovery of the Central Subspace for Regression Using the Influence Function of the Rényi Divergence Ross Iaci* and T. N. Sriram
6. SS-2022-0407 [Supp]	doi:10.5705/ss.202022.0407
Reinforcement Learning via Nonparametric Smoothing in a Continuous-Time Stochastic Setting With Noisy Data Chenyang Jiang, Bowen Hu, Yazhen Wang and Shang Wu*
7. SS-2022-0087 [Supp]	doi:10.5705/ss.202022.0087
The Importance of Being a Band: Finite-Sample Exact Distribution-Free Prediction Sets for Functional Data Jacopo Diquigiovanni*, Matteo Fontana and Simone Vantini
8. SS-2022-0312 [Supp]	doi:10.5705/ss.202022.0312
Enhanced Structural Break Detection in Functional Means Shuhao Jiao*, Ngai Hang Chan and Chun Yip Yau
9. SS-2022-0404 [Supp]	doi:10.5705/ss.202022.0404
A New Paradigm for Generative Adversarial Networks Based on Randomized Decision Rules Sehwan Kim, Qifan Song and Faming Liang*
10. SS-2023-0022 [Supp]	doi:10.5705/ss.202023.0022
Regularized Adaptive Huber Matrix Regression and Distributed Learning Yue Wang, Wenqi Lu, Lei Wang, Zhongyi Zhu, Hongmei Lin* and Heng Lian
11. SS-2022-0396 [Supp]	doi:10.5705/ss.202022.0396
Transfer Learning for High-Dimensional Quantile Regression via Convolution Smoothing Yijiao Zhang and Zhongyi Zhu*
12. SS-2023-0006 [Supp]	doi:10.5705/ss.202023.0006
Identification and Estimation of Treatment Effects on Long-Term Outcomes in Clinical Trials With External Observational Data Wenjie Hu, Xiao-Hua Zhou and Peng Wu*
13. SS-2022-0288 [Supp]	doi:10.5705/ss.202022.0288
A Data Fusion Method for Quantile Treatment Effects Yijiao Zhang and Zhongyi Zhu*
14. SS-2022-0336 [Supp]	doi:10.5705/ss.202022.0336
Empirical Likelihood Inference of Variance Components in Linear Mixed-Effects Models Jingru Zhang, Wei Guo, Joanne S. Carpenter, Andrew Leroux, Kathleen R. Merikangas, Nicholas G. Martin, Ian B. Hickie, Haochang Shou and Hongzhe Li*
15. SS-2022-0321 [Supp]	doi:10.5705/ss.202022.0321
Optimal Model Averaging for Single-Index Models With Divergent Dimensions Jiahui Zou, Wendun Wang, Xinyu Zhang* and Guohua Zou
16. SS-2022-0011 [Supp]	doi:10.5705/ss.202022.0011
Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials With Time-to-Event Outcomes Under the AFT Model Hongjian Zhu, Lixin Zhang*, Jing Ning and Lu Wang
17. SS-2022-0319 [Supp]	doi:10.5705/ss.202022.0319
Automatic Sparse PCA for High-Dimensional Data Kazuyoshi Yata* and Makoto Aoshima
18. SS-2022-0211 [Supp]	doi:10.5705/ss.202022.0211
Towards Optimal Use of Surrogate Markers to Improve Power Xuan Wang, Layla Parast, Lu Tian and Tianxi Cai*
19. SS-2022-0007 [Supp]	doi:10.5705/ss.202022-0007
The Tucker Low-Rank Classification Model for Tensor Data Junge Li, Qing Mai* and Xin Zhang
20. SS-2022-0366 [Supp]	doi:10.5705/ss.202022.0366
A Kernel Independence Test Using Projection-Based Measure in High-Dimension Yuexin Chen and Wangli Xu*
21. SS-2023-0128 [Supp]	doi:10.5705/ss.202023.0128
Unification of Rare and Weak Multiple Testing Models Using Moderate Deviations Analysis and Log-Chisquared P-values Alon Kipnis*
22. SS-2022-0388 [Supp]	doi:10.5705/ss.202022.0388
Cross Projection Test for High-Dimension Mean Vectors Guanpeng Wang and Hengjian Cui*
23. SS-2022-0323 [Supp]	doi:10.5705/ss.202022.0323
Inference for Change Points in High Dimensional Mean Shift Models Abhishek Kaul* and George Michailidis