### 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

### Education:

Education:

- Ph.D. in Statistics, Columbia University, 2013
- B.S., University of Science and Technology of China, 2008.

### Research Interests:

Research Interests:

- Latent variable models, psychometrics, cognitive diagnosis modeling
- Rare event analysis and simulation, high-dimensional statistics
- Semiparametric models, survival analysis

### Publications

Publications

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

**Some Preprints**

- Y. Gu and G. Xu. "Partial identifiability of restricted latent class models"

**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. - Y. Gu and G. Xu (2018+) "The sufficient and necessary condition for the identifiability and estimability of the DINA model."
*Psychometrika*, accepted. - G. Xu, S. Chiou, J. Yan, K. Marr, and C.-Y. Huang (2018+) "Generalized scale-change models for recurrent event processes under informative censoring."
*Statistica Sinica*, accepted. - C Wu, G Xu and W Pan (2018+) "An adaptive test on high-dimensional parameters in generalized linear models."
*Statistica Sinica*, accepted. - S. Chiou, C.-Y. Huang, G. Xu, and J. Yan (2018+) "Semiparametric regression analysis of panel count data: A practical review."
*International Statistical Review*, accepted.

**2018**

- Y. Gu, J. Liu, G. Xu and Z. Ying (2018) "Hypothesis Testing of the Q-matrix".
*Psychometrika*, 83(3) 515–537. - 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) - 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. - 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. - T Lyu, X Luo, G Xu, and C-Y Huang (2018) "Induced smoothing and efficient variance estimation for the accelerated gap time model with recurrent event data."
*Statistics in Medicine,*37(7) 1086-1100. - C Lee, C-Y Huang, G Xu, and X Luo (2018) "Semiparametric regression model for bivariate alternating recurrent event data.''
*Statistics in Medicine,*37(6) 996–1008.

**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") - 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. - 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.

**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 and earlier**

- 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 and other publications:**

- G. Xu (2018+) "Identifiability and Cognitive Diagnosis Models" Handbook of Diagnostic Classification Models -- State of the art in modeling, estimation, and applications.
- 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 (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*.

### PhD Students

PhD Students

- April Cho, U of Michigan
- Yuqi Gu, U of Michigan
- Yinqiu He, U of Michigan
- Chenchen Ma, U of Michigan
- Zhuoran Shang, U of Minnesota (graduated 2018)
- Juntao Wang (visiting phd student from Northeast Normal U.)

### Undergraduate Students

Undergraduate Students

- Xinyue Zhao
- Yanmeng Song
- William Zhang (currently CS PhD student @UPenn)
- Jiyang Wen (currently Biostats PhD student @Johns Hopkins)

### Teaching

Teaching

- Stats 413 Applied Regression Analysis. Winter 2018, Fall 2018
- Stats 601 Multivariate and Categorical Data Analysis. Winter 2017, Winter 2018

@ Minnesota

- Stat 5421 Categorical Data Analysis, Fall 2016
- Stat 3032 Regression and Correlated Data, Spring, Fall 2016
- Stat 8913 Literature Seminar, Fall 2015
- Stat 3032 Regression and Correlated Data, Fall 2015
- Stat 5302 Applied Linear Regression, Spring 2015
- Stat 5421 Categorical Data Analysis, Fall 2014
- Stat 5302 Applied Linear Regression, Spring 2014
- Stat 5302 Applied Linear Regression, Fall 2013