RESEARCH

Gongjun Xu

Assistant Professor of Statistics and Psychology (by courtesy)

University of Michigan

456 West Hall, 1085 South University, Ann Arbor, MI, 48109

E-mail: gongjun at umich edu

Phone: 734~647~9532

Research Interests:

Latent variable models, psychometrics, cognitive diagnosis modeling
High-dimensional statistics, rare event analysis
Semiparametric models, survival analysis

Education:

PhD in Statistics, Columbia University, 2013
B.S. University of Science and Technology of China (USTC), 2008.

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

Publications

Acknowledgement: research is/was supported by NSF, NSA, IES, and NIH

Submitted

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.

2018

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.

2017

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.

2016

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.

2015

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.

2014

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.

2013-12

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.