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Publications
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PUBLICATIONS

  1. Li, Z. and Dong, Y. (2025) A cepstral model for efficient spectral analysis of covariate-dependent time series. Journal of Computational and Graphical Statistics. Accepted. 
  2.  Soale, A. N. and Dong, Y. (2025) Envelope dimension reduction with application to binary classification. Journal of System Science and Complexity. Accepted. 
  3. Dong, Y. and Li, Z. (2024) A note on marginal coordinate test in sufficient dimension reduction. Statistics and Probability Letters, Volume 204, January 2024, 109947.
  4. Kai, B., Huang, M., Yao, W. and Dong, Y. (2023) Nonparametric and semiparametric quantile regression via a new MM Algorithm. Journal of Computational and Graphical Statistics, 32, 1613-1623. 
  5. Dong, Y., Soale, A. N. and Power, M. (2023) A selective review of sufficient dimension reduction for multivariate response regression. Journal of Statistical Planning and Inference, 226, 63-70.
  6. Soale, A. N. and Dong, Y. (2022) On sufficient dimension reduction via principal asymmetric least squares. Journal of Nonparametric Statistics, 34, 77-94.
  7. Zhou, T., Dong, Y. and Zhu, L. P. (2021) Testing the linear mean and constant variance conditions in sufficient dimension reduction. Statistica Sinica, 31, 2179-2194. 

  8. Power, M. and Dong, Y. (2021) Bayesian model averaging sliced inverse regression. Statistics and Probability Letters, Volume 174, July 2021, 109103. 

  9. Soale, A. N. and Dong, Y. (2021) On expectile-assisted inverse regression estimation for sufficient dimension reduction. Journal of Statistical Planning and Inference, 213, 80-92.

  10. Dong, Y. (2021) Sufficient dimension reduction through independence and conditional mean independence measures.  Springer Nature Switzerland AG, Efstathia Bura and Bing Li (eds.), Festschrift in Honor of R. Dennis Cook.

  11. Li, Z. and Dong, Y. (2021) Model-free variable selection with matrix-valued predictors. Journal of Computational and Graphical Statistics, 30, 171-181. 

  12. Artemiou, A., Dong, Y. and Shin, S. J. (2021) Real-time sufficient dimension reduction through principal least squares support vector machines. Pattern Recognition, Volume 112, April 2021, 107768.

  13. Dong, Y. (2021) A brief review of linear sufficient dimension reduction through optimization. Journal of Statistical Planning and Inference, 211, 154-161.  

  14. Dong, Y., Yu, Z. and Zhu, L. P. (2020) Model-free variable selection for conditional mean in regression. Computational Statistics and Data Analysis, Volume 152, December 2020, 107042. 

  15. Power, M. and Dong, Y. (2020) Comment on “Review of sparse sufficient dimension reduction” by Li, L., Wen, X. and Yu, Z. Statistical Theory and Related Fields, 4, 149-150.  

  16. Tang, C., Fang, X. and Dong, Y. (2020) High-dimensional interactions detection with sparse principal Hessian matrix. Journal of Machine Learning Research, 21 (19), 1-25.

  17. Shen, C., Chen, L., Dong, Y. and Priebe, C. E. (2020) Sparse representation classification beyond L1 minimization and the subspace assumption. IEEE Transactions on Information Theory, 66 (8), 5061-5071.

  18. Alothman, A., Dong, Y. and Artemiou, A. (2018) On dual model-free variable selection with two groups of variables. Journal of Multivariate Analysis, 167, 366-377.

  19. Bharadwaj, N. and Dong, Y. (2018) Comment on “Statistical challenges of administrative and transaction data” by Hand, D. J. Journal of the Royal Statistical Society, Series A, 181, 555-605.

  20. Dong, Y. and Li, Z. (2018) On sliced inverse regression with missing values. Journal of Nonparametric Statistics, 30, 990-1002.

  21. Dong, Y., Xia, Q., Tang, C. and Li, Z. (2018) On sufficient dimension reduction with missing responses through estimating equations. Computational Statistics and Data Analysis, 126, 67-77.

  22. Dong, Y. and Zhang, Y. (2018) On a new class of sufficient dimension reduction estimators. Statistics and Probability Letters, 139, 90-94.

  23. Bharadwaj, N., Noble, C., Tower, A., Smith, L. and Dong, Y. (2017) Predicting innovation success in the motion picture industry: the influence of multiple quality signals. Journal of Product Innovation Management, 34, 659-680.

  24. Dong, Y., Kai, B. and Yu, Z. (2017) Dimension reduction via local rank regression. Journal of Statistical Computation and Simulation, 87, 239-249.

  25. Xia, Q. and Dong, Y. (2017) On a new hybrid estimator for the central mean space. Journal of Systems Science and Complexity, 30, 111-121.

  26. Artemiou, A. and Dong, Y. (2016) Sufficient dimension reduction via principal Lq support vector machine. Electronic Journal of Statistics, 10, 783-805.

  27. Dong, Y. (2016) A note on moment-based sufficient dimension reduction estimators. Statistics and Its Interface, 9, 141-145.

  28. Dong, Y. and Yang, C. (2016) Cluster-based least absolute deviation regression for dimension reduction. Journal of Statistical Theory and Practice, 10, 121-132.

  29. Dong, Y., Yang, C. and Yu, Z. (2016) On permutation tests for predictor contribution in sufficient dimension reduction. Journal of Multivariate Analysis, 149, 81-91.

  30. Yu, Z. and Dong, Y. (2016) Model-free coordinate test and variable selection via directional regression. Statistica Sinica, 26, 1159-1174.

  31. Yu, Z., Dong, Y. and Shao, J. (2016) On marginal sliced inverse regression for ultrahigh dimensional model-free feature selection. The Annals of Statistics, 44, 2594-2623.

  32. Yu, Z., Dong, Y. and Zhu, L. X. (2016) Trace pursuit: a general framework for model-free variable selection. Journal of the American Statistical Association, 111, 813-821.

  33. Dong, Y. and Yu, Z. (2015) Direction estimation in a general regression model with discrete predictors.  In: Jin Z., Liu M., Luo X. (eds) New Developments in Statistical Modeling, Inference and Application. ICSA Book Series in Statistics. Springer, Cham.

  34.  Dong, Y., Yu, Z. and Zhu, L. P. (2015) Robust inverse regression for dimension reduction. Journal of Multivariate Analysis, 134, 71-81.

  35. Bharadwaj, N. and Dong, Y. (2014) Towards further understanding the market sensing capability-value creation relationship. Journal of Product Innovation Management, 31, 799–813.

  36. Yu, Z., Dong, Y. and Huang, M. (2014) General directional regression. Journal of Multivariate Analysis, 124, 94-104.

  37. Dong, Y., Yu, Z. and Sun, Y. (2013) A note on robust kernel inverse regression. Statistics and Its Interface, 6, 45-52.

  38. Dong, Y. and Zhu, L. P. (2013) Direction estimation in single-index model with missing values. Statistics and Its Interface, 6, 379-385.

  39. Yu, Z., Dong, Y. and Guo, R. (2013) On determining the structural dimension via directional regression. Statistics & Probability Letters, 83, 987-992.

  40. Zhu, L. P., Dong, Y. and Li, R. (2013) Semiparametric estimation of conditional heteroscedasticity via single-index modeling. Statistica Sinica, 23, 1235-1255.

  41. Dong, Y. and Yu, Z. (2012) Dimension reduction for the conditional kth moment via central solution space. Journal of Multivariate Analysis, 112, 207-218.

  42. Dong, Y. and Zhu, L. P. (2012) A note on sliced inverse regression with missing predictors. Statistical Analysis and Data Mining, 5, 128-138. 

  43. Dong, Y. and Li, B. (2010) Dimension reduction for non-elliptically distributed predictors: second-order methods. Biometrika, 97, 279-294.

  44. Dong, Y. and Zhu, L. P. (2010) Comment on “Envelope models for parsimonious and efficient multivariate linear regression”. Statistica Sinica, 20, 993-995.

  45. Yu, Z., Dong, Y. and Fang, Y. (2010) Marginal coordinate tests for central mean subspace with principal Hessian directions. Chinese Journal of Applied Probability and Statistics, 26, 544-552.

  46. Li, B. and Dong, Y. (2009) Dimension reduction for non-elliptically distributed predictors. The Annals of Statistics, 37, 1272-1298.

Address:1810 Liacouras Walk, Room 382 Philadelphia PA, 19122

Phone: 215-204-6878

Email: ydong@temple.edu