LSE creators

Number of items: 14.
2024
  • Chang, Jinyuan, Fang, Qin, Qiao, Xinghao, Yao, Qiwei (2024). On the Modeling and Prediction of High-Dimensional Functional Time Series. Journal of the American Statistical Association, 1-15. https://doi.org/10.1080/01621459.2024.2413201 picture_as_pdf
  • Fang, Qin, Guo, Shaojun, Qiao, Xinghao (2024). Adaptive functional thresholding for sparse covariance function estimation in high dimensions. Journal of the American Statistical Association, 119(546), 1473 - 1485. https://doi.org/10.1080/01621459.2023.2200522 picture_as_pdf
  • Liu, Yirui, Qiao, Xinghao, Pei, Yulong, Wang, Liying (2024). Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization. Proceedings of Machine Learning Research, 235, 31709-31727. picture_as_pdf
    • 2023
  • Liu, Yirui, Qiao, Xinghao, Wang, Liying, Lam, Jessica (2023). EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model. Proceedings of Machine Learning Research, 206, 2132-2146. picture_as_pdf
  • Chang, Jinyuan, Chen, Cheng, Qiao, Xinghao, Yao, Qiwei (2023). An autocovariance-based learning framework for high-dimensional functional time series. Journal of Econometrics, 239(2). https://doi.org/10.1016/j.jeconom.2023.01.007 picture_as_pdf
  • Guo, Shaojun, Qiao, Xinghao (2023). On consistency and sparsity for high-dimensional functional time series with application to autoregressions. Bernoulli, 29(1), 451 - 472. https://doi.org/10.3150/22-BEJ1464 picture_as_pdf
    • 2022
  • Qiao, Xinghao, Liu, Yirui, Lam, Jessica (2022). CATVI: conditional and adaptively truncated variational inference for hierarchical Bayesian nonparametric models. Proceedings of Machine Learning Research, 151, picture_as_pdf
  • Fang, Qin, Guo, Shaojun, Qiao, Xinghao (2022). Finite sample theory for high-dimensional functional/scalar time series with applications. Electronic Journal of Statistics, 16(1), 527 - 591. https://doi.org/10.1214/21-EJS1960 picture_as_pdf
  • Chen, Cheng, Guo, Shaojun, Qiao, Xinghao (2022). Functional linear regression: dependence and error contamination. Journal of Business and Economic Statistics, 40(1), 444 - 457. https://doi.org/10.1080/07350015.2020.1832503 picture_as_pdf
    • 2020
  • Qiao, Xinghao, Qian, Cheng, James, Gareth M., Guo, Shaojun (2020). Doubly functional graphical models in high dimensions. Biometrika, 107(2), 415 - 431. https://doi.org/10.1093/biomet/asz072 picture_as_pdf
    • 2019
  • Lian, Heng, Qiao, Xinghao, Zhang, Wenyang (2019). Homogeneity pursuit in single index models based panel data analysis. Journal of Business and Economic Statistics, https://doi.org/10.1080/07350015.2019.1665531
  • Qiao, Xinghao, Guo, Shaojun, James, Gareth M. (2019). Functional graphical models. Journal of the American Statistical Association, 114(525), 211 - 222. https://doi.org/10.1080/01621459.2017.1390466
    • 2015
  • Radchenko, Peter, Qiao, Xinghao, James, Gareth M. (2015). Index models for sparsely sampled functional data. Journal of the American Statistical Association, 110(510), 824 - 836. https://doi.org/10.1080/01621459.2014.931859
    • 2012
  • Qiao, Xinghao, James, Gareth M., Sun, Wenguang (2012). Comment. Technometrics, 54(2), 123-126. https://doi.org/10.1080/00401706.2011.649819