Selected Preprints (see also my Google Scholar)

•  J. Ouyang, C. Cui, K. M. Tan, and G. Xu. "Statistical Inference for Covariate-Adjusted and Interpretable Generalized Factor Model with Application to Testing Fairness"

• J. Li, G. Xu, and J. Zhu. “Statistical inference on latent space models for network data”.

• J. Li, G. Xu, and J. Zhu. “Hyperbolic network latent space model with learnable curvature”.

• J. Li, G. Xu, and J. Zhu. “High-dimensional factor analysis for network-linked data”.

• Y. Liu and G. Xu. “Clustering consistency of general nonparametric classification methods in cognitive diagnosis.”
  
  

2024 (+)

• Polo, F.M., Weber, L., Choshen, L., Sun, Y., Xu, G. and Yurochkin, M. (2024) “tinyBenchmarks: evaluating LLMs with fewer examples. International Conference on Machine Learning (ICML).

• Y. He, P. Song, and G. Xu (2024) "Adaptive Bootstrap Tests for Composite Null Hypotheses in the Mediation Pathway Analysis." Journal of the Royal Statistical Society Series B, 86, 411–434.

• M. Lin and G. Xu (2024) "Sufficient and necessary conditions for the identifiability of DINA models with polytomous responses." Psychometrika, 89(2), 717–740.

• C. Cui, C. Wang, and G. Xu. "Variational estimation for multidimensional generalized partial credit model." Psychometrika, accepted.

• S Xu, J Lu, J Zhang, C Wang, and G Xu. "Optimizing large-scale educational assessment with a divide-and-conquer strategy: fast and efficient distributed Bayesian inference in IRT Models." Psychometrika, accepted.

• C. Ma, J. Ouyang, C. Wang, and G. Xu (2024) "A note on improving variational estimation for multidimensional item response theory." Psychometrika, 89, 172–204.

• A. Cho, J. Xiao, C. Wang, and G. Xu (2024) "Regularized variational estimation for exploratory item factor analysis." Psychometrika, 89, 347–375.

• J. Xiao, C. Wang, and G. Xu. "A note on standard errors for multidimensional two-parameter logistic models using Gaussian variational estimation." Applied Psychological Measurement, accepted.

• J. Liu, X. Meng,  G. Xu,  W. Gao, N. Shi (2024) "MSAEM estimation for multidimensional four-parameter normal ogive models." Journal of Educational Measurement, 61(1), 99-124.

• A. Barthakur, J. Jovanovic, A. Zamecnik, V. Kovanovic, G. Xu, and S. Dawson (2024) "Towards Comprehensive Monitoring of Graduate Attribute Development: A Learning Analytics Approach in Higher Education." 14th International Conference on Learning Analytics and Knowledge (LAK24).
  
  

2023

• Y. Gu and G. Xu (2023) “A joint MLE approach to large-scale structured latent attribute analysis.” Journal of the American Statistical Association, 118(541), 746–760.

• S. M. Lee, T. Sit, and G. Xu (2023) "Efficient estimation for censored quantile regression." Journal of the American Statistical Association, 118(544), 2762-2775. 

• W. Tang, K. He, G. Xu, and J. Zhu (2023) "Survival analysis via ordinary differential equations." Journal of the American Statistical Association, 118(544), 2406-2421.

• J. Ouyang, K. M. Tan, and G. Xu (2023) "High-dimensional Inference for Generalized Linear Models with Hidden Confounding." Journal of Machine Learning Research, 24(296):1−61. 

• Y. Chen, C. Li, J. Ouyang, and G. Xu (2023) "Statistical inference for noisy incomplete binary matrix." Journal of Machine Learning Research, 24(95):1−66.

• Y. Gu, E. Erosheva, G. Xu, and D. Dunson (2023) "Dimension-grouped mixed membership models for multivariate categorical data." Journal of Machine Learning Research, 24(88):1−49.

• Y. Chen and G. Xu (2023) Discussion: “Vintage factor analysis with Varimax performs statistical inference” by Rohe and Zeng. Journal of the Royal Statistical Society Series B, 85: 1082-1084.

• Y. Chen, C. Li, J. Ouyang, and G. Xu (2023) "DIF Statistical Inference without Knowing Anchoring Items." Psychometrika, 88, 1097–1122.

• C. Ma, J. de la Torre, and G. Xu (2023) "Bridging parametric and nonparametric methods in Cognitive Diagnosis." Psychometrika, 88, 51–75.

• C. Ma, J. Ouyang, and G. Xu (2023) "Learning latent and hierarchical structures in cognitive diagnosis models." Psychometrika, 88, 175–207. 

• Z. Zeng, Y. Gu, and G. Xu  (2023) "A tensor-EM method for large-scale latent class analysis with binary responses." Psychometrika, 88, 580–612.

• X. Meng and G. Xu (2023) "A mixed stochastic approximation EM (MSAEM) algorithm for the estimation of the four-parameter Normal-Ogive model." Psychometrika, 88, 1407–1442. 

• X. Wang, G. Xu, and S. Zheng (2023) "Adaptive tests for bandedness of high-dimensional covariance matrices." Statistica Sinica, 33, 1673-1696.

• Y. Gu and G. Xu (2023) “Identifiability of hierarchical latent attribute models.” Statistica Sinica, 33, 2561-2591.

• C. Wang, R. Zhu, and G. Xu (2023) "Using lasso and adaptive lasso to identify DIF in multidimensional 2PL models." Multivariate Behavioral Research, 58, 387-407.

• S. Chiou, G. Xu, J. Yan, and C.-Y. Huang (2023) "Regression modeling for recurrent events possibly with an informative terminal event using R package reReg." Journal of Statistical Software, 105(1), 1–34.

• L. Zichi, T. Liu, E. Drueke, L. Zhao, and G. Xu (2023) "Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals." Scientific Report, 13, 6143.

• Chin, J-H., Ouyang, J., Fowler, R., Xu, G. and Matz, B. (2023). "Predicting Team Function Using Bayesian and Cognitive Diagnostic Modeling Approaches." Annual Conference of the American Society for Engineering Education.

• S. Culpepper and G. Xu (2023) "Introduction to JEBS Special Issue on Diagnostic Statistical Models." Journal of Educational and Behavioral Statistics, 48, 687–689.
  
  

2022

• X. Zhang, G. Xu, and J. Zhu (2022) "Joint latent space models for network data with high-dimensional node variables." Biometrika, 109(3), 707–720.

• C. Li, C. Ma, and G. Xu (2022) "Learning large Q-matrix by restricted Boltzmann machines." Psychometrika, 87, 1010–1041.

• J. Ouyang and G. Xu (2022) "Identifiability of latent class models with covariates." Psychometrika, 87, 1343–1360.

• Y Deng, Y He, G Xu, and W Pan (2022) "Speeding up Monte Carlo simulations for the adaptive sum of powered score test with importance sampling." Biometrics, 78(1), 261-273.  

• C. Ma and G. Xu (2022) "Hypothesis testing for hierarchical structures in cognitive diagnosis models." Journal of Data Science, 20(3), 279-302.

• J Wang, N Shi, X Zhang, and G Xu (2022) "Sequential Gibbs sampling algorithm for cognitive diagnosis models with many attributes." Multivariate Behavioral Research, 57(5), 840-858. 

• T. Liu, C. Wang, and G. Xu (2022) "Estimating three- and four-parameter MIRT models with importance sampling enhanced variational autoencoder." Frontiers in Psychology, 13:935419.

• C. Li, N. Wang, and G. Xu (2022) "Inference for optimal differential privacy procedures for frequency tables." Journal of Data Science, 20(2), 253-276.

• Erosheva, E. A., Minhas, S., Xu, G., and Xu, R. (2022). "Editorial: Data Science Meets Social Sciences". Journal of Data Science, 20(3), 277-278.

2021

• Y He, G Xu, C Wu, and W Pan (2021) “Asymptotically independent U-statistics in high-dimensional testing.” Annals of Statistics, 49, 154-181.

• Y He, B Meng, Z Zeng, and G Xu (2021) "On the phase transition of Wilks' phenomenon." Biometrika, 108(3), 741–748. 

• C.W. Chu, T. Sit, and G. Xu (2021) “Transformed dynamic quantile regression on censored data.” Journal of the American Statistical Association, 116(534), 874-886.

• Y He, Z Wang, and G Xu (2021) "A note on the Likelihood Ratio Test in high-dimensional exploratory Factor Analysis." Psychometrika, 86(2), 442-463. 

• Z Shang, E Erosheva, and G Xu (2021) "Partial-Mastery Cognitive Diagnosis Models." Annals of Applied Statistics, 15(3), 1529-1555. 

• Y. He, T. Jiang, J. Wen, and G. Xu (2021) “Likelihood ratio test in multivariate linear regression: from low to high dimension.” Statistica Sinica,  31, 1215-1238.

• Y. Gu and G. Xu (2021) “Sufficient and necessary conditions for the identifiability of the Q-matrix.” Statistica Sinica,  31, 449-472.

• A. Cho, C. Wang, X. Zhang and G. Xu (2021) "Gaussian variational estimation for multidimensional item response theory." British Journal of Mathematical and Statistical Psychology, 74, 52-85.
  
  

2020

• Y. Gu and G. Xu (2020) “Partial identifiability of restricted latent class models.”  Annals of Statistics, 48(4), 2082-2107. 

• J. Wang, X. He, and G. Xu (2020) “Debiased inference on treatment effect in a high dimensional model.” Journal of the American Statistical Association, 115 (529), 442-454. 

• C Wu, G Xu, X Shen, and W Pan (2020) “A regularization-based adaptive test for high-dimensional generalized linear models.” Journal of Machine Learning Research, 21 (128), 1-67.

• G. Xu,  S. Chiou, J. Yan, K.  Marr, and C.-Y. Huang (2020) "Generalized scale-change models for recurrent event processes under informative censoring."  Statistica Sinica,  30, 1773-1795. 

• X. Meng, G. Xu, J. Zhang, and J. Tao (2020) “Marginalized maximum a posteriori estimation for the 4PL model under a mixture modeling framework.” British Journal of Mathematical and Statistical Psychology, 73(S1), 51-82. 

• N-H Choi,  K Shedden, G Xu, X Zhang, and J Zhu (2020) "Comment: ridge regression, ranking variables and improved principal component regression." Technometrics, 62 (4), 451-455. 

• W Jin, ... , L Zhao (2020) "Observation of the polaronic character of excitons in a two-dimensional semiconducting magnet CrI3". Nature Communications, 11, 4780.
  
  

2019

• 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  (2019) "The sufficient and necessary condition for the identifiability and estimability of the DINA model." Psychometrika, 84(2), 468-483. 

• 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. 

• C Wu, G Xu and W Pan (2019) "An adaptive test on high-dimensional parameters in generalized linear models." Statistica Sinica, (29), 2163-2186. 

• 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 (2019) "Identifiability and Cognitive Diagnosis Models".  Book chapter in Handbook of Diagnostic Classification Models edited by von Davier, M. and Lee, Y.-S.  
  
  

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. 

• 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.

• 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.

• 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.

• G. Xu, Z. Wu and S. Murphy (2018) "Micro-randomized Trials ." Wiley StatsRef: Statistics Reference Online.
  
  

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.

• 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.
  
  

2013-12

• 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.

• 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).  Rare-event Simulation for Exponential Integrals of Smooth Gaussian Processes.  Proceedings of the 2012 Winter Simulation Conference.