Book

Cover of Linear Models Theory for Data Science
  • Hong, Y., Deng, X., and Morgan, J. P. Linear Models Theory for Data Science . Springer Texts in Statistics, Springer.
  • This graduate-level textbook presents linear models as a foundation for a broad range of methods in statistics, machine learning, and data science. It develops the theory of standard linear models and then connects it to optimization, correlated-response models, regularization, non-normal responses, predictive modeling, and nonlinear relationships. The book combines theoretical foundations with data-analysis examples, computing, and exercises to help readers understand both the connections among these methods and their practical implementation.

Selected Publications

See Google Scholar for a complete and more up-to-date list.

  1. Hong, Y. and Gu, X. (2026), Statistical and Deep Learning Approaches for Predicting Degradation of Polymeric Materials in Photovoltaics, Journal of Quality Technology, in press.
  2. Song, X., Lee, L., Xie, K., Liu, X., Deng, X., and Hong, Y. (2026), StatLLM: A Dataset for Evaluating the Performance of Large Language Models in Statistical Analysis, Scientific Data, in press.
  3. Min, J., Song, X., Zheng, S., King, C. B., Deng, X., and Hong, Y. (2026), Applied Statistics in the Era of Artificial Intelligence: A Review and Vision, Applied Stochastic Models in Business and Industry, in press.
  4. Meeker, W. Q., Escobar, L. A., Pascual, F. G., Hong, Y., Liu, P., Falk, W. M., and Ananthasayanam, B. (2026), Modern Statistical Models and Methods for Estimating Fatigue-Life and Fatigue-Strength Distributions from Experimental Data, Statistical Science, Vol. 41, pp. 1-27.
  5. Zheng, S., Lu, L., Hong, Y., and Liu, J. (2026), Planning Reliability Assurance Tests for Autonomous Vehicles Based on Disengagement Events Data, IISE Transactions, Vol. 58, pp. 131-146.
  6. Min, J., Hong, Y., Meeker, W. Q., and Ostrouchov, G. (2025), A Spatially Correlated Competing Risks Time-to-Event Model for Supercomputer GPU Failure Data, Technometrics, Vol. 67, pp. 531-545.
  7. Wang, Y., Hong, Y., Deng, X., and Freeman, L. (2025), The Use of Variational Inference for Lifetime Data with Spatial Correlations, Journal of Quality Technology, in press.
  8. Zheng, S., Clark, J. M., Salboukh, F., Silva, P., da Mata, K., Pan, F., Min, J., Lian, J., King, C. B., Fiondella, L., Liu, J., Deng, X., and Hong, Y. (2025), DR-AIR: A Data Repository Bridging the Research Gap in AI Reliability, Quality Engineering, in press.
  9. Cho, Y., Hong, Y., and Du, P. (2025), An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions, Computational Statistics and Data Analysis, Vol. 208, 108161.
  10. Song, X., Odongo, K., Pascual, F. G., and Hong, Y. (2025), A Comprehensive Study on the Performance of Machine Learning Methods on the Classification of Solar Panel Electroluminescence Images, Journal of Quality Technology, Vol. 57, pp. 93-109.
  11. Do, Q., Cho, Y., Du, P., and Hong, Y. (2024), Reliability Study of Battery Lives: A Functional Degradation Analysis Approach, The Annals of Applied Statistics, Vol. 18, pp. 3185-3204.
  12. Xu, L., Hong, Y., Morris, M., and Cameron, K. (2024), Prediction for Distributional Outcomes in High-Performance Computing I/O Variability, Journal of the Royal Statistical Society: Series C, Vol. 73, pp. 561-580.
  13. Hong, Y., Lian, J., Xu, L., Min, J., Wang, Y., Freeman, L., and Deng, X. (2023), Statistical Perspectives on Reliability of Artificial Intelligence Systems, Quality Engineering, Vol. 35, pp. 56-78.
  14. Min, J., Hong, Y., King, C. B., and Meeker, W. Q. (2022), Reliability Analysis of Artificial Intelligence Systems Using Recurrent Events Data from Autonomous Vehicles, Journal of the Royal Statistical Society: Series C, Vol. 71, pp. 987-1013.
  15. Lian, J., Freeman, L., Hong, Y., and Deng, X. (2021), Robustness with Respect to Class Imbalance in Artificial Intelligence Classification Algorithms, Journal of Quality Technology, Vol. 53, pp. 505-525.
  16. Lu, L., Wang, B. X., Hong, Y., and Ye, Z. (2021), General Path Models for Multivariate Degradation Data with Repeated Measures and Covariates, Technometrics, Vol. 63, pp. 354-369.
  17. Fang, G., Pan, R., and Hong, Y. (2020), Copula-based Reliability Analysis of Degrading Systems with Dependent Failures, Reliability Engineering & System Safety, Vol. 193, 106618.
  18. Xie, Y., Xu, L., Li, J., Deng, X., Hong, Y., Kolivras, K. N., and Gaines, D. N. (2019), Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence, Journal of the American Statistical Association, Vol. 114, pp. 1466-1480.
  19. Lee, I., Hong, Y., Tseng, S. T., and Dasgupta, T. (2018), Sequential Bayesian Design for Accelerated Life Tests, Technometrics, Vol. 60, pp. 472-483.
  20. 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.
  21. 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.
  22. Duan, Y., Hong, Y., Meeker, W. Q., Stanley, D. L., and Gu, X. (2017), Photodegradation Modeling Based on Laboratory Accelerated Test Data and Predictions Under Outdoor Weathering for Polymeric Materials, The Annals of Applied Statistics, Vol. 11, pp. 2052-2079.
  23. 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.
  24. 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.
  25. Meeker, W. Q. and Hong, Y. (2014), Reliability Meets Big Data: Opportunities and Challenges (with discussion), Quality Engineering, Vol. 26, pp. 102-116.
  26. Hong, Y. and Meeker, W. Q. (2013), Field-Failure Predictions Based on Failure-Time Data with Dynamic Covariate Information, Technometrics, Vol. 55, pp. 135-149.
  27. Hong, Y. (2013), On Computing the Distribution Function for the Poisson Binomial Distribution, Computational Statistics and Data Analysis, Vol. 59, pp. 41-51.
  28. 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.
  29. Hong, Y. and Meeker, W. Q. (2010), Field-Failure and Warranty Prediction Using Auxiliary Use-rate Data, Technometrics, Vol. 52, pp. 148-159.
  30. 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, No. 2, pp. 857-879.