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Published Papers

I have published papers in the following four categories:

  • Journal papers on Statistics and Econometrics

  • Conference papers on Machine Learning and Artificial Intelligence

  • Journal papers on Geography with Statistical Applications

  • Others

Journal papers on Statistics and Econometrics

  1. M. Yang, M. Li and G. Li (2024+), On memory-augmented gated recurrent unit network, International Journal of Forecasting. In press.

  2. H. Yuan, K. Lu and G. Li (2024+), Volatility Analysis with High-frequency and Low-frequency Historical Data, and Options-Implied Information, Statistica Sinica. In press.

  3. X. Zhang, G. Li, C.C. Liu and J. Guo (2024+), Tucker tensor factor models: Matricization and mode-wise PCA estimation, Scinece in China, Series A. In press.

  4. D. Wang, Y. Zheng and G. Li (2024), High-dimensional low-rank tensor autoregressive time series modeling, Journal of Econometrics 238, 105544.

  5. H. Yuan, K. Lu, G. Li and J. Wang (2024), High-Frequency-Based Volatility Model with Network Structure, Journal of Time Series Analysis 45, 533-557.

  6. Q. Zhu, S. Tan, Y. Zheng and G. Li (2023), Quantile autoregressive conditional heteroscedasticity, Journal of the Royal Statistical Society, Series B 85, 1099-1127.

  7. Y. Gao, X. Zhu, H. Qi, G. Li, R. Zhang and H. Wang (2023), An asymptotic analysis of random partition based minibatch momentum methods for linear regression models, Journal of Computational and Graphical Statistics 32, 1083-1096.

  8. X. Zhang, D. Wang, H. Lian and G. Li (2023), Nonparametric quantile regression for homogeneity pursuit in panel data models, Journal of Business & Economic Statistics 41, 1238-1250.

  9. Y. Zheng, J. Wu, W.K. Li and G. Li (2023), Least absolute deviations estimation for nonstationary vector autoregressive time series models with pure unit roots, Statistics and Its Interface 16, 199-216.

  10. Pan, R., Ren, T., Guo, B., Li, F., Li, G. and Wang, H. (2022), A note on distributed quantile regression by pilot sampling and one-step updating, Journal of Business & Economic Statistics 40, 1691-1700.

  11. Zhu, Q. and Li, G. (2022), Quantile double autoregression, Econometric Theory 38, 793–839.

  12. Wang, D., Zheng, Y., Lian, H. and Li, G. (2022), High-dimensional vector autoregressive time series modeling via tensor decomposition, Journal of the American Statistical Association 117, 1338-1356. (GitHub)

  13. Wang, G., Zhu, K., Li, G. and Li, W.K. (2022), Hybrid Quantile Estimation for Asymmetric Power GARCH Models, Journal of Econometrics 227, 264-284.

  14. Zhang, Y., Lian, H., Li, G. and Zhu, Z. (2021), Functional additive quantile regression, /Statistica Sinica 31, 1331-1351.

  15. Zhu, Q., Li, G. and Xiao, Z. (2021), Quantile Estimation of Regression Models with GARCH-X Errors, Statistica Sinica 31, 1261-1284.

  16. Cai, Y. and Li, G. (2021), A quantile function approach to the distribution of financial returns following TGARCH models, Statistical Modelling 21, 189–219.

  17. Li, D., Zeng, R., Zhang, L., Li, W.K. and Li, G. (2020), Conditional quantile estimation for hysteretic autoregressive models, Statistica Sinica 30, 809-824.

  18. Zhu, Q., Zeng, R. and Li, G. (2020), Bootstrap inference for GARCH models by the least absolute deviation estimation, Journal of Time Series Analysis 41, 21-40.

  19. Dong, C., Li, G. and Feng, X. (2019), Lack-of-fit tests for quantile regression models, Journal of the Royal Statistical Society, Series B 81, 629-648. (GitHub)

  20. Wu, J., Li, G. and Xia, Q. (2018), Moment-based tests for random effects in the two-way error component model with unbalanced panels, Economic Modelling 74, 61-76.

  21. Zhu, Q., Zheng, Y. and Li, G. (2018), Linear double autoregression, Journal of Econometrics 207, 162-174.

  22. Zheng, Y., Zhu, Q., Li, G. and Xiao, Z. (2018), Hybrid quantile regression estimation for time series models with conditional heteroscedasticity, Journal of the Royal Statistical Society, Series B 80, 975-993. (R codes)

  23. Zheng, Y., Li, W.K. and Li, G. (2018), A robust goodness-of-fit test for generalized autoregressive conditional heteroscedastic models, Biometrika 105, 73-89.

  24. Zhu, X., Pan, R., Li, G., Liu, Y. and Wang, H. (2017), Network vector autoregression, Annals of Statistics 45, 1096–1123.

  25. Li, G., Zhu, Q., Liu, Z. and Li, W.K. (2017), On mixture double autoregressive time series models, Journal of Business & Economic Statistics 35, 306-317.

  26. Zheng, Y., Li, Y. and Li, G. (2016), On Frechet autoregressive conditional duration models, Journal of Statistical Planning and Inference 175, 51-66.

  27. Lo, P.H., Li, W.K., Yu, P.L.H. and Li, G. (2016), On buffered threshold GARCH models, Statistica Sinica 26, 1555-1567.

  28. Li, G., Guan, B., Li, W.K. and Yu, P.L.H. (2015), Hysteretic autoregressive time series models, Biometrika 102, 717-723.

  29. Li, M., Li, W.K. and Li, G. (2015), A new hyperbolic GARCH model, Journal of Econometrics 189, 428-436.

  30. Li, G., Li, Y. and Tsai, C.-L. (2015), Quantile correlations and quantile autoregressive modeling, Journal of the American Statistical Association 110, 246-261.

  31. Liu, S. and Li, G. (2015), Varying-coefficient mean-covariance regression analysis for longitudinal data, Journal of Statistical Planning and Inference 160, 89-106.

  32. Wu, J. and Li, G. (2014), Moment-based tests for individual and time effects in panel data models, Journal of Econometrics 178, 569-581.

  33. Li, D., Li, G. and You, J. (2014), Significant variable selection and autoregressive order determination for time series partially linear models, Journal of Time Series Analysis 35, 478-490.

  34. Li, G., Leng, C. and Tsai, C.-L. (2014), A hybrid bootstrap approach to unit root tests, Journal of Time Series Analysis 35, 299-321.

  35. Li, M., Li, W.K. and Li, G. (2013), On mixture memory GARCH models, Journal of Time Series Analysis 34, 606-624.

  36. Kwan, W., Li, W.K. and Li, G. (2012) On the estimation and diagnostic checking of the ARFIMA–HYGARCH model, Computational Statistics and Data Analysis 56, 3632-3644.

  37. Li, G. and Li, W.K. (2011), Testing a linear time series model against its threshold extension, Biometrika 98, 243-250.

  38. Li, M., Li, G. and Li, W.K. (2011), Score tests for hyperbolic GARCH models, Journal of Business & Economic Statistics 29, 579-586.

  39. Kwan, W., Li, W.K. and Li, G. (2011), On the threshold hyperbolic GARCH models, Statistics and Its Interface 4, 159-166.

  40. Li, G. and Li, W.K. (2009), Least absolute deviation estimation for unit root processes with GARCH errors, Econometric Theory 25, 1208-1227.

  41. Li, G. and Li, W.K. (2008), Testing for threshold moving average with conditional heteroscedasticity, Statistica Sinica 18, 647-665.

  42. Li, G. and Li, W.K. (2008), Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity, Biometrika 95, 399-414.

  43. Wang, H., Li, G. and Jiang, G. (2007), Robust regression shrinkage and consistent variable selection via the LAD-LASSO, Journal of Business & Economic Statistics 25, 347-355.

  44. Wang, H., Li, G. and Tsai, C.-L. (2007), Regression coefficients and autoregressive order shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B 69, 63-78.

  45. Li, G. and Li, W.K. (2005), Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach, Biometrika 92, 691-701.

  46. Pan, J., Li, G. and Xie, Z. (2002), Stationary solution and parametric estimation for bilinear model driven by ARCH noises, Science in China, Series A 45, 1523-1537.

Conference papers on Machine Learning and Artificial Intelligence

  1. F. Huang, K. Lu, Y. Cai, Z. Qin, Y. Fang, G. Tian & G. Li (2023), Encoding recurrence into transformers, Proceedings of the 11th International Conference on Learning Representations (ICLR-23). (The acceptance rate is 31.8%, and this is an oral paper, i.e. notable-top-5%)

  2. Y. Fang, Y. Cai, J. Chen, J. Zhao, G. Tian & G. Li (2023), Cross-layer retrospective retrieving via layer attention, Proceedings of the 11th International Conference on Learning Representations (ICLR-23). (The acceptance rate is 31.8%)

  3. J. Zhao, Y. Fang & G. Li (2021), Recurrence along depth: deep convolutional neural networks with recurrent layer aggregation, Advances in Neural Information Processing Systems (NeurIPS 2021). Vol. 34, pp.10627-10640. (GitHub, the acceptance rate is 26%.)

  4. W. Tu, P. Liu, Y. Liu, L. Kong, G. Li, B. Jiang, H. Yao, and S. Jui (2021), Nonsmooth Low-rank Matrix Recovery: Methodology, Theory and Algorithm, Proceedings of the Future Technologies Conference (FTC 2021), Vol. 1, pp 848–862.

  5. J. Zhao, F. Huang, J. Lv, Y. Duan, Z. Qin, G. Li & G. Tian (2020), Do RNN and LSTM have long memory? Proceedings of the 37th International Conference on Machine Learning (ICML-20). Vol. 119, pp.11365-11375. (The acceptance rate is 21.8%.)

  6. D. Wang, F. Huang, J. Zhao, G. Li & G. Tian (2020), Compact autoregressive network, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20). pp.6145-6152. (The acceptance rate is 20.6%)

  7. P. Liu, W. Tu, J. Zhao, Y. Liu, L. Kong, G. Li, B. Jiang, G. Tian and H. Yao (2019) M-estimation in low-rank matrix factorization: a general framework, Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM-19). pp. 568-577. (Regular paper with the acceptance rate of 9.08%).

  8. W. Tu, D. Yang, L. Kong, M. Che, Q. Shi, G. Li and G. Tian (2019), Ensemble-based Ultrahigh-dimensional Variable Screening, Proceedings of the 28th International Joint Conferences on Artifical Intelligence (IJCAI-19), pp. 3613-3619. (The acceptance rate is 17.8%)

Journal papers on Geography

  1. Q. Pei, M. Qiu, G. Li, K.M. Wu, L. Mordechai, W. Liu and H. Zhang (2024), Cost of resilience to climate change: migration, conflicts, and epidemics in imperial China, Environmental Research Letters 19, 114025.

  2. D.D. Zhang, Q. Pei, H.F. Lee, C.Y. Jim, G. Li, M. Zhang, J. Li, Z. Wu, L. Wang, R.P.H. Yue and S. Zhang (2020), Cultural dynamics of human resilience under climate change in Europe of past 2,500 years, Science of the Total Environment 744, 140842.

  3. Q. Pei, G. Li, B.P. Winterhalder and M. Lowman (2020), Regional patterns of pastoralist migrations under the push of reduced precipitation in imperial China, Global Ecology and Biogeography 29, 433-443.

  4. Q. Pei, Z. Nowak, G. Li, C. Xu and W.K. Chan (2019), The Strange Flight of the Peacock: Farmers’ atypical northwesterly migration from central China, 200BC-1400AD, Annals of the Association of American Geographers 109, 1583-1596. (The flagship journal of AAG)

  5. Q. Pei, D.D. Zhang, G. Li, P. Foret and H.F. Lee (2016), Temperature and precipitation effects on agrarian economy in late imperial China, Environmental Research Letters 11, 064008.

  6. Q. Pei, D.D. Zhang, H.F. Lee and G. Li (2016), Crop management as an agricultural adaptation to climate in early modern era: A comparative study of Eastern and Western Europe, Agriculture 6, 29.

  7. Q. Pei, D.D. Zhang, G. Li and H.F. Lee (2015), Climate change and the macroeconomic structure in pre-industrial Europe: new evidence from wavelet analysis, PLoS ONE 10(6), e0126480.

  8. Q. Pei, D.D. Zhang, G. Li, B. Winterhalder and H.F. Lee (2015), Epidemics in Ming and Qing China: impacts of changes of climate andeconomic well-being, Social Science & Medicine 136-137, 73-80.

  9. Q. Pei, D.D. Zhang, H.F. Lee and G. Li (2014), Climate change and macro-economic cycles in pre-industrial Europe, PLoS ONE 9(2), e88155.

  10. Q. Pei, D.D. Zhang, G. Li and H.F. Lee (2013), Short and long term impacts of climate variations on the agrarian economy in pre-industrial Europe, Climate Research 56, 169-180.

Book Chapter and Invited Discussions

  1. Zheng, Y., Li, Y., Li, W.K. and Li, G. (2016), Diagnostic checking for Weibull autoregressive conditional duration models. In: Li, W.K., Stanford, D.A., Yu, H. (editors): Advances in Time Series Methods and Applications: the A. Ian McLeod Festschrift, 107-114, Springer-Verlag, New York.

  2. Yu, P.L.H. and Li, G. (2014), Discussion on the paper “Principal volatility component analysis”, Journal of Business & Economic Statistics 32, 166-167.

  3. Li, W.K. and Li, G. (2009), Discussion on the paper “Model selection for generalized linear models with factor-augmented predictors”, Applied Stochastic models in Business and Industry 25, 237-239.

  4. Li, W.K. and Li, G. (2009), Discussion on the paper “Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearest block bootstrap approach”, Science in China, Series A 52, 1109-1110.