Publications
Gao, Y., Zhang, Z., Cai, Z., Zhu, X.*, Zou, T., & Wang, H. (2024). Penalized sparse covariance regression with high dimensional covariates. Journal of Business & Economic Statistics, online [arXiv Preprint]
Gao, Y., Pan, R.*, Li, F., Zhang, R., & Wang, H. (2024). Grid point approximation for distributed nonparametric smoothing and prediction. Journal of Computational and Graphical Statistics, online [arXiv Preprint]
Lin, Z., Gao, Y., Wang, F.*, & Wang, H. (2025). Testing sufficiency for transfer learning. Computational Statistics & Data Analysis, 108075. [arXiv Preprint]
Qi, H., & Gao, Y.* (2024). Communication‐efficient distributed gradient descent via random projection. Stat, 13(4), e70030.
Li, X., Gao, Y.*, Chang, H., Huang, D., Ma, Y., Pan, R., Qi, H., Wang, F., Wu, S., Xu, K., Zhou, J., Zhu, X., Zhu, Y., & Wang, H. (2024). A selective review on statistical methods for massive data computation: distributed computing, subsampling, and minibatch techniques. Statistical Theory and Related Fields, 8(3), 163-185. [arXiv Preprint]
Shi, J., Wang, F.*, Gao, Y., Song, X., & Wang, H. (2024). Mixture conditional regression with ultrahigh dimensional text data for estimating extralegal factor effects. The Annals of Applied Statistics, 18(3), 2532-2550. [arXiv Preprint]
Ren, Y., Li, Z., Zhu, X.*, Gao, Y., & Wang, H. (2024). Distributed estimation and inference for spatial autoregression model with large scale networks. Journal of Econometrics, 238(2), 105629. [arXiv Preprint]
Gao, Y., Zhu, X.*, Qi, H., Li, G., Zhang, R., & Wang, H. (2023). An asymptotic analysis of random partition based minibatch momentum methods for linear regression models. Journal of Computational and Graphical Statistics, 32(3), 1083-1096. [arXiv Preprint]
Gao, Y., Liu, W., Wang, H., Wang, X., Yan, Y., & Zhang, R.* (2022). A review of distributed statistical inference. Statistical Theory and Related Fields, 6(2), 89-99. [arXiv Preprint]
Gao, Y., Zhang, R.*, & Wang, H. (2022). On the asymptotic properties of a bagging estimator with a massive dataset. Stat, 11(1), e485. [arXiv Preprint]
Zhu, Y., Huang, D.*, Gao, Y., Wu, R., Chen, Y., Zhang, B., & Wang, H. (2021). Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation. Neural Networks, 141, 11-29. [arXiv Preprint]