Yili Hong

•  STAT 3615 Biological Statistics
• STAT 4106 Theoretical Statistics
• STAT 5124 Linear Model Theory
• STAT 5454 Statistical Methods for Reliability Data
• STAT 5594 Deep Learning and Applications in Survival Analysis
•  STAT 5605 Biometry/Biostatistics I
• STAT 5606 Biometry/Biostatistics II
• STAT 5684 Survival Analysis
• STAT 5694 Longitudinal Data Analysis

• Machine Learning and Engineering Applications
• Reliability Analysis; Safety of Artificial Intelligence Systems
• Survival Analysis, Longitudinal Data Analysis; Biostatistics
• Spatial Data Analysis; Epidemiology
• Statistical Computing

Selected publications and a complete and up-to-date list is available at Google Scholar.

• Lian, J., Freeman, L., Hong, Y., and Deng, X. (2021), Investigating the Robustness of Artificial Intelligence Algorithms with Mixture Experiments, Journal of Quality Technology, Vol 53, pp. 505-525.
• Xu, L., Gotwalt, C., Hong, Y., King, C. B., and Meeker, W. Q., (2020), Applications of the Fractional-Random-Weight Bootstrap, The American Statistician, Vol. 74, pp. 345-358.
• Xu, L., Wang, Y., Lux, T., Chang, T., Bernard, J., Li, B., Hong, Y., Watson, L., and Cameron, K. (2020), Modeling I/O performance variability in high- performance computing systems using mixture distributions, Journal of Parallel and Distributed Computing, Vol. 139, pp. 87-98.
• Xie, Y., Xu, L., Li, J., Deng, X., Hong, Y., Kolivras, K. N., and David N. Gaines (2019), Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence, Journal of the American Statistical Association, Vol. 114, pp. 1466-1480.
• Yuan, M., Tang, C., Hong, Y., and Yang, J. (2018), Disentangling and Assessing Uncertainties in Multiperiod Corporate Default Risk Predictions, The Annals of Applied Statistics, Vol. 12, pp. 2587-2617.
• Lee, I., Hong, Y., Tseng, S. T., and Dasgupta, T. (2018), Sequential Bayesian Design for Accelerated Life Tests, Technometrics, Vol. 60, pp. 472-483.
• Hong, Y., Zhang, M., and Meeker, W. Q., (2018), Big Data and Reliability Applications: The Complexity Dimension, Journal of Quality Technology, Vol. 50, pp. 135-149.
• Xie, Y., King, C., Hong, Y., and Yang, Q. (2018), Semiparametric Models for Accelerated Destructive Degradation Test Data Analysis, Technometrics, Vol. 60, pp. 222-234.
• Li, J., Hong, Y., Thapa, R., and Burkhart, H. E. (2015), Survival Analysis of Loblolly Pine Trees with Spatially Correlated Random Effects, Journal of the American Statistical Association, Vol. 110, pp. 486-502.
• Hong, Y., Duan, Y., Meeker, W. Q., Stanley, D. L., and Gu, X. (2015),
  Statistical Methods for Degradation Data with Dynamic Covariates
  Information and an Application to Outdoor Weathering Data, Technometrics,
  Vol. 57, pp. 180-193.
• Meeker, W. Q. and Hong, Y. (2014), Reliability Meets Big Data: Opportunities and Challenges (with discussion), Quality Engineering, Vol. 26, pp. 102-116.
• Hong, Y. (2013), On Computing the Distribution Function for the Poisson Binomial Distribution, Computational Statistics and Data Analysis, Vol. 59, pp. 41-51.
• Yang, Q., Hong, Y., Chen, Y., and Shi, J. (2012), Failure Profile Analysis of a Single Repairable System Using Trend-renewal Process, IEEE Transactions on Reliability, Vol. 61, pp. 180-191.
• Al-Khalidi, H. R., Hong, Y., Fleming, T. R., and Therneau, T. (2011), Insights on the Robust Standard Error Under Recurrent Events Model, Biometrics, Vol. 67, pp. 1564-1572.
• Hong, Y., Meeker, W. Q., and McCalley, J. D. (2009), Prediction Intervals for Remaining Life of Power Transformers Based on Left Truncated and Right Censored Lifetime Data, The Annals of Applied Statistics, Vol. 3, pp. 857-879.