Jiming Jiang, Ph.D.

Education

Ph.D. Statistics (UC Berkeley, 1995)

Research Interests

• Mixed effects models
• Generalized linear models
• Model selection
• Small area estimation
• Big Data intelligence
• Precision medicine
• Pharmacokinetics
• Asymptotic theory
• Spatial statistics
• Testing hypotheses
• Longitudinal data analysis
• Matrix algebra
• Numerical analysis
• Monte Carlo methods
• Reliability

Selected Works

• Jiang, J. (2017), Asymptotic Analysis of Mixed Effects Models: Theory, Application, and Open Problems, Chapman & Hall/CRC. (link)
• Jiang, J., Rao, J. S., Fan, J., and Nguyen, T. (2018), Classified mixed model prediction, Journal of the American Statistical Association 113, 269-279. (link)
• Jiang, J. and Rao, J. S. (2020), Robust small area estimation: An overview, Annual Review of Statistics and Its Applications 7, 337-360. (link)
• Jiang, J. and Torabi, M. (2020), Sumca: Simple, unified, Monte-Carlo assisted approach to second-order unbiased MSPE estimation, Journal of the Royal Statistical Society, Series B 82, 467-485. (link)
• Jiang, J., Feuer, E. J., Li, Y., Nguyen, T., and Yu, M. (2021), Inference about Age-standardized Rates with Sampling Errors in the Denominators, Statistical Methods in Medical Research 30, 535–548. (link)
• Jiang, J. and Nguyen, T. (2021), Linear and Generalized Linear Mixed Models and Their Applications, 2nd ed., Springer, New York. (link)
• Jiang, J., Wand, M. P. and Bhaskaran, A. (2022), Usable and precise asymptotics for generalized linear mixed model analysis and design, Journal of the Royal Statistical Society, Series B 84, 55–82. (link)
• Jiang, J. (2022), Large Sample Techniques for Statistics, 2nd ed., Springer, New York. (link)