Publications

ARTICLES & PAPERS

      1. Chakraborty, S. and Su, Z. (2023). A Comprehensive Bayesian Framework for Envelope Models. Journal of the American Statistical Association, to appear.
      2. Su, Z., Li, B. and Cook, R. D. (2023). Envelope Model for Function-on-function Linear Regression. Journal of Computational and Graphical Statistics, to appear.
      3. Zhao, S., Su, Z. Liu, H., Khoo, C., Garrett, T. J. and Gu, L. (2023). Predictive models built upon annotated and validated intake biomarkers in urine using paired or unpaired analysis helped to classify cranberry juice consumers in a randomized, double-blinded, placebo-controlled, and crossover study. Nutrition Research, 109, 58-70.
      4. Khare, K. and Su, Z. (2022).  Response Variable Selection in Multivariate Linear Regression. Statistica Sinica, to appear.
      5. Park, Y., Su, Z. and Chung, D. (2022). Envelope-based Partial Partial Least Squares with Application to Cytokine-based Biomarker Analysis for COVID-19.  Statistics in Medicine, 41(23), 4578-4592.
      6. Qiu, Y., Fall, T., Su, Z., Bortolozo, F., Mussoline, W., England, G., Dinkins, D., Morgan, K., Clark, M. and Liu, G. (2022). Effect of Phosphorus Fertilization on Yield of Chipping Potato Grown on High Legacy Phosphorus Soil. Agronomy, 12(4):812.  https://doi.org/10.3390/agronomy12040812
      7. Liu, L., Li, W., Su, Z., Cook, R. D., Vizioli, L. and Yacoub, E. (2022). Efficient Estimation via Envelope Chain in MRI-based Studies. Scandinavian Journal of Statistics, 49(2),481 – 501.
      8. Li, M., Kong, L. and Su, Z. (2021). Double Fused Lasso Regularized Regression with Both Matrix and Vector Valued Predictors. Electronic Journal of Statistics, 15, 1909-1950.
      9. Lee, M., Chakraborty, S. and Su, Z. (2021). A Bayesian approach to envelope quantile regression. Statistica Sinica, 32, 2339-2357.
      10. Ding, S., Su, Z., Zhu, G. and Wang, L. (2021). Envelope Quantile Regression. Statistica Sinica, 31, 79-106.
      11. Forzani, L. and Su, Z. (2021). Envelopes for Elliptical Multivariate Linear Regression. Statistica Sinica, 31, 301-332.
      12. Lee, M. and Su, Z. (2020). A Review of Envelope Models.  International Statistical Review, 88, 658-676.
      13. Zhao, S., Liu, H., Su, Z., Khoo, C. and Gu, L. (2020). Identifying Cranberry Juice Consumers with Predictive OPLS-DA Models of Plasma Metabolome and Validation of Cranberry Juice Intake Biomarkers in a Double-Blinded, Randomized, Placebo-controlled, Cross-over Study. Molecular Nutrition & Food Research, Doi: 10.1002/mnfr.201901242.
      14. Liu, H., Garrett, T., Su, Z., Khoo, C., Zhao, S. and Gu, L. (2020). Modifications of Urinary Metabolome in Young Women after Cranberry Juice Consumption Were Revealed Using UHPLC-Q-Orbitrap-HRMS-Based Metabolomics Approach. Food & Function.  Doi: 10.1039/c9fo02266j.
      15. Chen, T., Su, Z., Yang, Y. and Ding, S. (2020). Efficient estimation in expectile regression using envelope models. Electronic Journal of Statistics, 14(1), 143-173.
      16. Zhu, G. and Su, Z. (2020). Envelope-based Sparse Partial Least Squares. Annals of Statistics, 48, 161-182.
      17. Park, Y., Su, Z. and Zhu, H. (2017), Groupwise Envelope Models for Imaging Genetics Analysis. Biometrics, 73, 1243-1253.
      18. Liu, H., Garrettb, T. J., Su, Z., Khoo, C., and Gu, L. (2017), UHPLC-Q-Orbitrap-HRMS-based Global Metabolomics Reveal Metabolome Modifications in Plasma of Young Women After Cranberry Juice Consumption. Journal of Nutritional Biochemistry, 45, 67-76.
      19. Su, Z., Zhu, G., Chen, X. and Yang, Y. (2016), Sparse Envelope Model: Efficient Estimation and Response Variable Selection in Multivariate Linear Regression. Biometrika. 103, 579-593.
      20. Liu, H., Tayyari, F., Edison, A., Su, Z. and Gu, L. (2016), NMR-Based Metabolomics Reveals Urinary Metabolome Modifications in Female Sprague-Dawley Rats by Cranberry Procyanidins. Journal of Nutritional Biochemistry. 34, 136-145.
      21. Cook, R. D., Forzani, L. and Su, Z. (2016), A Note on Fast Envelope Estimation. Journal of Multivariate Analysis. 150, 42-54.
      22. Khare, K., Pal, S. and Su, Z. (2017), A Bayesian Approach for Envelope Models. Annals of Statistics. 45, 196-222.
      23. Cook, R. D. and Su, Z. (2016), Scaled Predictor Envelopes and Partial Least Squares Regression. Technometrics, 58, 155-165.
      24. Liao, X., Su, Z., Liu. G., Zotarelli, L., Cui, Y. and Snodgrass, C. (2016), Impact of Soil Moisture and Temperature on Potato Production Using Seepage and Center Pivot Irrigation. Agricultural Water Management. 165, 230-236.
      25. Cook, R. D., Su, Z. and Yang, Y. (2015), envlp: A MATLAB Toolbox for Computing Envelope Estimators in Multivariate Linear Regression. Journal of Statistical Software. Doi: 10.18637/jss.v062.i08.
      26. Cook, R. D. and Su, Z. (2013), Scaled Envelopes: Scale Invariant and Efficient Estimation in Multivariate Linear Regression. Biometrika. 100, 921-938.
      27. Cook, R. D., Helland, I. S. and Su, Z. (2013), Envelopes and Partial Least Squares Regression. Journal of the Royal Statistical Society: Series B., 75, 851-877.
      28. Su, Z. and Cook, R. D. (2013), Estimation of Multivariate Means with Heteroscedastic Error Using Envelope Models. Statistica Sinica, 23, 213-230.
      29. Su, Z. and Cook, R. D. (2012), Inner Envelopes: Efficient Estimation in Multivariate Linear Regression. Biometrika, 99, 687-702.
      30. Su, Z. and Cook, R. D. (2011), Partial Envelopes for Efficient Estimation in Multivariate Linear Regression, Biometrika, 98, 133-146.
      31. Cook, R. D., Li, B., Chiaromonte, F. and Su, Z. (2010), Rejoinder of “Envelope Models for Parsimonious and Efficient Multivariate Linear Regression”, Statistica Sinica, 20, 999-1010.