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[1] S. Song, T. Wang, G. Shen, Y. Lin & J. Huang (2025+). Wasserstein generative regression, Journal of the Royal Statistical Society, Series B, in press. [2] S. Song, Y. Lin & Y. Zhou (2024). Semi-supervised inference for block-wise missing data without imputation, Journal of Machine Learning Research, 25(99): 1-36. [3] S. Song, Y. Lin & Y. Zhou (2023). A general M-estimation theory in semi-supervised framework, Journal of the American Statistical Association,119(546): 1065-1075. [4] L. Shao, S. Song & Y. Zhou (2023). Optimal subsampling for large sample quantile regression with massive data, Canadian Journal of Statistics, 51(2): 420-433. [5] P. Liu, S. Song & Y. Zhou (2022). Semiparametric varying-coefficient additive hazard model for clustered failure time Data with frailty effects, Canadian Journal of Statistics, 50(2): 549-571. [6] S. Song, Y. Lin & Y. Zhou (2021). Linear expectile regression under massive data, Fundamental Research, 1: 574-585.
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