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Biography

Prof. Jianguo Sun

Department of Statistics

University of Missouri, USA


Email: sunj@missouri.edu


Qualifications

1992 Ph.D., University of Waterloo, Canada

1986 M.S., Jilin University, China

1983 B.S., Jilin University, China


Publications (Selected)

  1. X. Zhao, N. Balakrishnan and J. Sun. (2011) Nonparametric Inference Based on Panel Count Data. Test, 20, 1-71.
  2. K. H. Lee, S. Chakraborty and J. Sun. (2011) Bayesian Variable Selection in Semiparametric Proportional Hazards Model for High Dimensional Survival Data. The International Journal of Biostatistics, 7, Article 21.
  3. L. Zhu, J. Sun, X. Tong and S. Pounds. (2011) Regression Analysis of Longitudinal Data with Informative Observation Times and Application to Medical Cost Data. Statistics in Medicine, 30, 1429-1440.
  4. N. Li, L. Sun and J. Sun. (2010) Semiparametric Transformation Models for Panel Count Data with Dependent Observation Processes. Statistics in Biosciences, 2, 191-210.
  5. Z. Zhang and J. Sun. (2010) Interval Censoring. Statistical Methods in Medical Research, 19, 53-70.
  6. L. Zhu, J. Sun, X. Tong and D. K. Srivastava. (2010) Regression Analysis of Multivariate Recurrent Event Data with a Dependent Terminal Event. Lifetime Data Analysis, 16, 478-490.
  7. L. Chen and J. Sun. (2010) A Multiple Imputation Approach to the Analysis of Intervalcensored Failure Time Data with the Additive Hazards Model. Computational Statistics and Data Analysis, 54, 1109-1116.
  8. L. Chen and J. Sun. (2010) Multiple Imputation to Regression Analysis of Intervalcensored Failure Time Data with Linear Transformation Models. Far East Journal of Theoretical Statistics, 33, 41-55.
  9. Y. Li, A. Suchy and J. Sun. (2010) Nonparametric Treatment Comparison for Current Status Data. Journal of Biometrics & Biostatistics, 1, 102.
  10. L. Wang, J. Sun and X. Tong. (2010) Regression Analysis of Case II Interval-censored Failure Time Data with the Additive Hazards Model. Statistica Sinica, 20, 1709-1723.
  11. X. Zhang and J. Sun. (2010) Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size. Computational Statistics and Data Analysis, accepted.
  12. J. Sun and J. Shen. (2009) Efficient Estimation for the Proportional Hazards Model with Competing Risks and Current Status Data. Canadian Journal of Statistics, 37, 592-606.
  13. L. Sun, L. Zhu and J. Sun (2009) Regression Analysis of Multivariate Recurrent Event Data with Time-varying Covariate Effects. Journal of Multivariate Analysis, 100, 2214-2223.
  14. D. Deng, H. Fang and J. Sun. (2009) Nonparametric Estimation for Doubly Censored Failure Time Data. Journal of Nonparametric Statistics, 21, 801-814.
  15. X. Tong, X. He, L. Sun and J. Sun. (2009) Variable Selection for Panel Count Data via Nonconcave Penalized Estimating Function. Scandinavian Journal of Statistics, 36, 620-635.
  16. P. Chen, J. Shen and J. Sun. (2009) Statistical Analysis of Clustered Current Status Data. International Journal of Intelligent Technologies and Applied Statistics, 2, 21-31.
  17. P. Chen, R. He, J. Shen and J. Sun. (2009) Regression Analysis of Right-censored Failure Time Data with Missing Censoring Indicators. Acta Mathematicae Applicatae Sinica, 25, 415-426.
  18. B. Zhang, X. Tong and J. Sun. (2009) Efficient Estimation for the Proportional Odds Model with Bivariate Current Status Data. Far East Journal of Theoretical Statistics, 27, 113-132.
  19. X. He, X. Tong and J. Sun. (2009) Semiparametric Analysis of Panel Count Data with Correlated Observation and Follow-up Times. Lifetime Data Analysis, 15, 177-196.
  20. X. Tong, L. Zhu and J. Sun. (2009) Variable Selection for Recurrent Event Data via Nonconcave Penalized Estimating Function. Lifetime Data Analysis, 15, 197-215.
  21. L. Chen and J. Sun. (2009) A Multiple Imputation Approach to the Analysis of Current Status Data with the Additive Hazards Model. Communications in Statistics: Theory and Methods, 38, 1009-1018.
  22. L. Zhu, J. Sun and P. Wood. (2009) Statistical Methods for the Analysis of Alcohol and Drug Uses for Young Adults. Journal of Data Science, 7, 469-485.
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