Review/Overview Articles and Books
- Luo, X., Dasgupta, T., Xie, M., & Liu, R. (2020). Leveraging the Fisher randomization test using confidence distributions: inference, combination and fusion learning. Journal of the Royal Statistical Society: Series B (in press).
- Shafer, G., & Vovk, V. (2019). Game-Theoretic Foundations for Probability and Finance (Vol. 455). John Wiley & Sons.
- Schweder, T., & Hjort, N. L. (2016). Confidence, likelihood, probability (Vol. 41). Cambridge University Press.
- Efron, B., & , Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press.
- 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.
- Liu, K., & Meng, X. L. (2016). There is individualized treatment. Why not individualized inference?. Annual Review of Statistics and Its Application, 3, 79-111.
- Reid, N., & Cox, D. R. (2015). On some principles of statistical inference. International Statistical Review, 83(2), 293-308.
- Martin, R., & Liu, C. (2015). Inferential models: reasoning with uncertainty. Chapman and Hall/CRC.
- Balasubramanian, V., Ho, S. S., & Vovk, V. (Eds.). (2014). Conformal prediction for reliable machine learning: theory, adaptations and applications. Newnes.
- Berger, J. O. (2013). Statistical decision theory and Bayesian analysis. Springer Science & Business Media.
- Xie, M. G., & Singh, K. (2013). Confidence distribution, the frequentist distribution estimator of a parameter: A review. International Statistical Review, 81(1), 3-39. [Dicussion and Rejoinder]
- Robert, C. (2007). The Bayesian choice: from decision-theoretic foundations to computational implementation. Springer Science & Business Media.
Publications
2019 and beyond
- Luo, X., Dasgupta, T., Xie, M., & Liu, R. (2020). Leveraging the Fisher randomization test using confidence distributions: inference, combination and fusion learning. Journal of the Royal Statistical Society: Series B (in press).
- Efron, B. (2020). Prediction, Estimation and Attrition (with discussion). Journal of the American Statistical Association (in press).
- Xie, M. G., & Zheng, Z. (2020). Homeostasis phenomenon in predictive inference when using a wrong learning model: a tale of random split of data into training and test sets. arXiv preprint arXiv:2003.08989.
- Gao, Q., Lai, R. C., Lee, T. C., & Li, Y. (2019). Uncertainty Quantification for High-Dimensional Sparse Nonparametric Additive Models. Technometrics, 1-12.
- Gong, R. (2019). Simultaneous Inference Under the Vacuous Orientation Assumption. PMLR 103:225-234
- Williams, J. P., Xie, Y., & Hannig, J. (2019). The EAS approach for graphical selection consistency in vector autoregression models. arXiv preprint arXiv:1906.04812.
- Neupert, S. D., & Hannig, J. (2019). BFF: Bayesian, Fiducial, Frequentist Analysis of Age Effects in Daily Diary Data. The Journals of Gerontology: Series B.
- Cui, Y., & Hannig, J. (2019). Estimation and testing of survival functions via generalized fiducial inference with censored data, with discussion and rejoinder by the authors. Biometrika, 106, 501-518.
- Liu, Y., Hannig, J., & Majumder, A. P. (2019). Second-Order Probability Matching Priors for the Person Parameter in Unidimensional IRT Models. Psychometrika, 84(3), 701-718.
- Williams, J. P., & Hannig, J. (2019). Nonpenalized variable selection in high-dimensional linear model settings via generalized fiducial inference. The Annals of Statistics, 47(3), 1723-1753.
- Shen, J., Liu, R. Y., & Xie, M. G. (2019). iFusion: Individualized Fusion Learning. Journal of the American Statistical Association (in press)
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.
2014-2015
- Reid, N., & Cox, D. R. (2015). On some principles of statistical inference. International Statistical Review, 83.2: 293-308.
- Berger, J. O., Bernardo, J. M., & Sun, D. (2015). Overall objective priors. Bayesian Analysis, 10(1): 189-221.
- Lai, R. C., Hannig, J., & Lee, T. C. (2015). Generalized fiducial inference for ultrahigh-dimensional regression. Journal of the American Statistical Association, 110(510), 760-772.
- Liu, D., Liu, R. Y., & Xie, M. (2015). Multivariate meta-analysis of heterogeneous studies using only summary statistics: efficiency and robustness. Journal of the American Statistical Association, 110(509), 326-340.
- Claggett, B., Xie, M., & Tian, L. (2014). Meta-analysis with fixed, unknown, study-specific parameters. Journal of the American Statistical Association, 109(508), 1660-1671.
- Chen, X., & Xie, M. G. (2014). A split-and-conquer approach for analysis of extraordinarily large data. Statistica Sinica, 1655-1684.
- Hannig, J., Lai, R. C., & Lee, T. C. (2014). Computational issues of generalized fiducial inference. Computational Statistics & Data Analysis, 71, 849-858.
2013 and before
- Xie, M. G., & Singh, K. (2013). Confidence distribution, the frequentist distribution estimator of a parameter: A review. International Statistical Review, 81(1), 3-39.
- Hannig, J. (2013). Generalized fiducial inference via discretization. Statistica Sinica, 489-514.
- Berger, J. O., Bernardo, J. M., & Sun, D. (2012). Objective priors for discrete parameter spaces. Journal of the American Statistical Association, 107(498), 636-648.
- Bayarri, M. J., Berger, J. O., Forte, A., & García-Donato, G. (2012). Criteria for Bayesian model choice with application to variable selection. The Annals of Statistics, 40(3), 1550-1577.
- Hannig, J., & Xie, M. G. (2012). A note on Dempster-Shafer recombination of confidence distributions. Electronic Journal of Statistics, 6, 1943-1966.
- Cisewski, J., & Hannig, J. (2012). Generalized fiducial inference for normal linear mixed models. The Annals of Statistics, 40(4), 2102-2127.
- Wang, C. M., Hannig, J., & Iyer, H. K. (2012). Fiducial prediction intervals. Journal of Statistical Planning and Inference, 142(7), 1980-1990.
- Wandler, D. V., & Hannig, J. (2012). Generalized fiducial confidence intervals for extremes. Extremes, 15(1), 67-87.
- Wandler, D. V., & Hannig, J. (2012). A fiducial approach to multiple comparisons. Journal of Statistical Planning and Inference, 142(4), 878-895.
- Xie, M., Singh, K., & Strawderman, W. E. (2011). Confidence distributions and a unifying framework for meta-analysis. Journal of the American Statistical Association, 106(493), 320-333.
- Wandler, D. V., & Hannig, J. (2011). Fiducial inference on the largest mean of a multivariate normal distribution. Journal of Multivariate Analysis, 102(1), 87-104.
- Maruyama, Y., & Strawderman, W. E. (2010). Robust Bayesian variable selection with sub-harmonic priors. arXiv preprint arXiv:1009.1926.
- Berger, J. O., Bernardo, J. M., & Sun, D. (2009). The formal definition of reference priors. The Annals of Statistics, 37(2), 905-938.
- Xie, M., Singh, K., & Zhang, C. H. (2009). Confidence intervals for population ranks in the presence of ties and near ties. Journal of the American Statistical Association, 104(486), 775-788.
- Hannig, J. (2009). On generalized fiducial inference. Statistica Sinica, 19(2), 491.
- Hannig, J., & Lee, T. C. (2009). Generalized fiducial inference for wavelet regression. Biometrika, 96(4), 847-860.
- Lidong, E., Hannig, J., & Iyer, H. (2008). Fiducial intervals for variance components in an unbalanced two-component normal mixed linear model. J Am Stat Assoc, 103(482), 854-865.
- Liang, F., Paulo, R., Molina, G., Clyde, M. A., & Berger, J. O. (2008). Mixtures of g priors for Bayesian variable selection. Journal of the American Statistical Association, 103(481), 410-423.
- George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553-580.
- Hannig, J., Iyer, H., & Patterson, P. (2006). Fiducial generalized confidence intervals. Journal of the American Statistical Association, 101(473), 254-269.
- Berger, J. O., & Pericchi, L. R. (1996). The intrinsic Bayes factor for model selection and prediction. Journal of the American Statistical Association, 91(433), 109-122.