2016-2018
Lai, R., Hannig, J., & Lee, T. (2018). Method G: Uncertainty Quantification for Distributed Data Problems using Generalized Fiducial Inference. arXiv preprint arXiv:1805.07427.
Hannig, J., Feng, Q., Iyer, H., Wang, C. M., & Liu, X. (2018). Fusion learning for inter-laboratory comparisons. Journal of Statistical Planning and Inference, 195, 64-79.
Fraser, D. A. S., Reid, N., & Lin, W. (2018). When should modes of inference disagree? Some simple but challenging examples. The Annals of Applied Statistics, 12(2), 750-770.
Liu, S., Tian, L., Lee, S., & Xie, M. G. (2018). Exact inference on meta-analysis with generalized fixed-effects and random-effects models. Biostatistics & Epidemiology, 2(1), 1-22.
Thornton, S., Li, W., & Xie, M. G. (2017). An effective likelihood-free approximate computing method with statistical inferential guarantees. arXiv preprint arXiv:1705.10347.
Liu, Y., & Hannig, J. (2017). Generalized fiducial inference for logistic graded response models. psychometrika, 82(4), 1097-1125.
Liu, K., & Meng, X. L. (2016). There is individualized treatment. Why not individualized inference?. Annual Review of Statistics and Its Application, 3, 79-111.
Hannig, J., Iyer, H., Lai, R. C., & Lee, T. C. (2016). Generalized fiducial inference: A review and new results. Journal of the American Statistical Association, 111(515), 1346-1361.
Yang, G., Liu, D., Wang, J., & Xie, M. G. (2016). Meta‐analysis framework for exact inferences with application to the analysis of rare events. Biometrics, 72(4), 1378-1386.