- Latent variable models, psychometrics, cognitive diagnosis modeling
- High-dimensional statistics, rare event analysis
- Semiparametric models, survival analysis
Honors and Awards:
Honors and Awards:
- NSF CAREER Award, 2019
- Outstanding Young Researcher Award, International Chinese Statistical Association (ICSA), 2019
- Bernoulli Society New Researcher Award, 2019
- ICSA China Conference Junior Researcher Paper Award, 2018
- NSA Mathematical Sciences Program Young Investigator Award, 2016
- IES Statistical and Research Methodology in Education Early Career grant (with Chun Wang), 2016
- Nonparametric Statistics Section Student Paper Award, ASA, 2011
- Laha Travel Award, IMS, 2011
- Guo Moruo Scholarship, USTC, 2007
Acknowledgement: research is/was supported by NSF, NSA, IES, and NIH
- Y He, G Xu, C Wu, and W Pan. “Asymptotically independent U-statistics in high-dimensional testing.”
- Y. He, T. Jiang, J. Wen, and G. Xu. “Likelihood ratio test in multivariate linear regression: from low to high dimension.”
- C Wu, G Xu, X Shen, and W Pan. “A regularization-based adaptive test for high-dimensional generalized linear models.”
- C.W. Chu, T. Sit, and G. Xu. “Transformed dynamic quantile regression on censored data.”
- Z. Shang, E. Erosheva, and G.Xu. “Partial-mastery cognitive diagnosis models.”
- A. Cho, C. Wang, X. Zhang and G. Xu. "Gaussian variational estimation for Multidimensional Item Response Theory".
- Y. Deng, Y. He, G. Xu, and W. Pan. "Speeding up Monte Carlo simulations for adaptive tests with importance sampling".
- Y. Gu and G. Xu. "Identification and estimation of hierarchical latent attribute models."
2019 & Accepted
- Y. Gu and G. Xu (2019) “Learning attribute patterns in high-dimensional structured latent attribute models.” Journal of Machine Learning Research, 20(115):1−58.
- Y. Gu and G. Xu. “Partial identifiability of restricted latent class models.” Annals of Statistics, accepted.
- C. Wang, G. Xu, and X. Zhang (2019) “Correction for item response theory latent trait measurement error in linear mixed effects models.” Psychometrika, 84(3), 673–700.
- Y. Gu and G. Xu. “Sufficient and necessary conditions for the identifiability of the Q-matrix.” Statistica Sinica, accepted.
- J. Wang, X. He, and G. Xu. “Debiased inference on treatment effect in a high dimensional model.” Journal of the American Statistical Association, accepted.
- G. Xu, S. Chiou, J. Yan, K. Marr, and C.-Y. Huang "Generalized scale-change models for recurrent event processes under informative censoring." Statistica Sinica, accepted.
- C Wu, G Xu and W Pan "An adaptive test on high-dimensional parameters in generalized linear models." Statistica Sinica, accepted.
- X. Meng, G. Xu, J. Zhang, and J. Tao. “Marginalized maximum a posteriori estimation for the 4PL model under a mixture modeling framework.” British Journal of Mathematical and Statistical Psychology, accepted.
- Y. Gu and G. Xu (2019) "The sufficient and necessary condition for the identifiability and estimability of the DINA model." Psychometrika, 84(2), 468-483.
- S. Chiou, C.-Y. Huang, G. Xu, and J. Yan (2019) "Semiparametric regression analysis of panel count data: A practical review." International Statistical Review, 87(1), 24–43.
- G Xu and Z Shang (2018) "Identifying Latent Structures in Restricted Latent Class Models." Journal of the American Statistical Association, 113(523), 1284-1295. (supplementary file)
- Y. Gu, J. Liu, G. Xu and Z. Ying (2018) "Hypothesis Testing of the Q-matrix". Psychometrika, 83(3) 515–537.
- C Wang, G Xu, and Z Shang (2018) “A two-stage approach to differentiating normal and aberrant behavior in computer based testing.” Psychometrika, 83(1) 223–254.
- Y He and G Xu (2018) "Estimating tail probabilities of the ratio of the largest eigenvalue to the trace of a Wishart matrix." Journal of Multivariate Analysis, 166, 320–334.
- X Li and G Xu (2018) "Uniformly efficient simulation for extremes of Gaussian random fields". Journal of Applied Probability, 55(1), 157-178.
- S. Chiou, G. Xu, J. Yan, and C.-Y. Huang (2018) "Semiparametric estimation of the accelerated mean model with panel count data under informative examination times.'' Biometrics, 74(3) 944-953.
- T Lyu, X Luo, G Xu, and C-Y Huang (2018) "Induced smoothing for rank-based regression with recurrent gap time data." Statistics in Medicine, 37(7) 1086-1100.
- C Lee, C-Y Huang, G Xu, and X Luo (2018) "Semiparametric regression analysis for alternating recurrent event data.'' Statistics in Medicine, 37(6) 996–1008.
- C. Wang, G. Xu, Z. Shang and N. Kuncel (2018). "Detecting aberrant behavior and item pre-knowledge." Journal of Educational and Behavioral Statistics, 43(4) 469-501.
- G. Xu (2017). “Identifiability of Restricted Latent Class Models with Binary Responses.” Annals of Statistics, 45(2), 675-707.
- T Jiang, K Leder and G Xu (2017) “Rare-event analysis for extremal eigenvalues of white Wishart matrices.” Annals of Statistics, 45(4), 1609-1637.
- G. Xu, T. Sit, L.Wang and C.-Y. Huang (2017) “Estimation and inference of quantile regression for survival data under biased sampling.” Journal of the American Statistical Association, 112 (520), 1571-1586,.
- G. Xu, S. Chiou, C.-Y. Huang, M.-C. Wang and J. Yan (2017) “Joint scale-change models for recurrent events and failure time”. Journal of the American Statistical Association, 112 (518), 794-805. (R package "reReg")
- S. Chiou and G. Xu (2017), Rank-based estimation for semiparametric accelerated failure time model under length biased sampling. Statistics and Computing, 27(2), 483-500.
- Z Xu, G Xu and W Pan (2017) "Adaptive testing for association between two random vectors in moderate to high dimensions." Genetic Epidemiology, 41, 599–609.
- Y Chen, X Li, J Liu, G Xu and Z Ying (2017). "Exploratory item classification via spectral graph clustering.'' Applied Psychological Measurement, 41, 579 - 599.
- G. Xu, L. Lin, P. Wei, and W. Pan (2016) “An adaptive two-sample test for high-dimensional means.” Biometrika, 103(3), 609–624. (R package "highmean")
- G. Xu and S. Zhang (2016). “Identifiability of Diagnostic Classification Models.” Psychometrika, 81(3), 625-649.
- G. Xu, C. Wang, and Z. Shang (2016) “On initial item selection in cognitive diagnosis computerized adaptive testing.” British Journal of Mathematical and Statistical Psychology, 69(3), 291–315.
- X. Li, J. Liu and G. Xu (2016) On the Tail Probabilities of Aggregated Lognormal Random Fields with Small Noise, Mathematics of Operations Research, 41, 236-246.
- X Luo, M Li, G Xu and D Tu (2016) Survival analysis following dynamic randomization. Contemporary Clinical Trials Communications, 3, 39-47.
- Y. Chen, J. Liu, G. Xu, and Z. Ying (2015). Statistical Analysis of Q-matrix Based Diagnostic Classification Models. Journal of the American Statistical Association, 110, 850-866.
- C. Wang and G. Xu (2015). “A mixture hierarchical model for response times and response accuracy,” British Journal of Mathematical and Statistical Psychology, 68,456-477.
- C. Wang, Z. Shu, Z. Shang, and G. Xu (2015). Assessing item-Level fit for the DINA model. Applied Psychological Measurement, 39(7), 525–538.
- B. Sen and G. Xu (2015), Model Based Bootstrap Methods for Interval Censored Data, Computational Statistics and Data Analysis. 81, 121–129.
- J. Liu and G. Xu (2014). On the Conditional Distributions and the Efficient Simulations of Exponential Integrals of Gaussian Random Fields. Annals of Applied Probability 24(4), 1691–1738.
- G. Xu, G. Lin, and J. Liu (2014) Rare-event Simulation for the Stochastic Korteweg-de Vries Equation, SIAM/ASA Journal on Uncertainty Quantification 2-1, 698-716.
- J. Liu and G. Xu (2014). Efficient Simulations for the Exponential Integrals of Hölder Continuous Gaussian Random Fields. The ACM Transactions on Modeling and Computer Simulation, 24(2), 9:1–9:24.
- G. Xu, B. Sen and Z. Ying (2014). Bootstrapping a Change-Point Cox Model for Survival Data. Electronic Journal of Statistics 8, 1345-1379.
- J. Liu, G. Xu and Z. Ying (2013). Theory of Self-Learning Q-matrix. Bernoulli 19(5A), 1790-1817.
- X. Luo, G. Xu and Z. Ying (2013). Sequential Analysis of the Cox Model under Response Dependent Allocation. Statistica Sinica. 23(4), 1761-1774.
- J. Liu and G. Xu (2013). On the Density Functions of Integrals of Gaussian Random Fields. Advances in Applied Probability. 45(2), 398-424.
- J. Liu, G. Xu and Z. Ying (2012). Data-driven learning of Q-matrix. Applied Psychological Measurement. 36(7), 548-564.
- J. Liu and G. Xu (2012). Some Asymptotic Results of Gaussian Random Fields with Varying Mean Functions and the Associated Processes. Annals of Statistics. 40(1), 262-293.
Refereed conference papers, book chapters, and other publications:
- G. Xu (2019) "Identifiability and Cognitive Diagnosis Models". Book chapter in Handbook of Diagnostic Classification Models edited by von Davier, M. and Lee, Y.-S.
- G. Xu, Z. Wu and S. Murphy (2018) "Micro-randomized Trials ." Wiley StatsRef: Statistics Reference Online.
- J Liu and G Xu (2018) “Cognitive Diagnosis.” The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation.
- G. Xu (2018) Book Review “Biased Sampling, Over-identified Parameter Problems and Beyond” by Jing Qin, LIDA newsletter, vol 3(2).
- G. Xu (2014) Uniformly Efficient Simulation for Tail Probabilities of Gaussian Random Fields. Proceedings of the 2014 Winter Simulation Conference.
- F Wang, G Xu, Y Zhang and L Ma (2014) “Red cell distribution width is associated with presence, stage and grade in patients with renal cell carcinoma,” Disease Markers, vol. 2014, 860419.
- J. Liu and G. Xu (2012). Rare-event Simulation for Exponential Integrals of Smooth Gaussian Processes. Proceedings of the 2012 Winter Simulation Conference.