Research

Research Interests

  • Latent Variable Modeling and Mixture Models
  • Gaussian Processes and Computer Models
  • Subgroup Analysis and Variable Selection
  • State Space Models and Time Series Data Analysis
  • Nonparametric and Semiparametric Models
  • Missing Data and Measurement Error Models
  • Mobile Health and Reinforcement Learning
  • Shape Constrained Estimation
  • Objective Bayes

Support

My research group is currently supported by NSF CAREER Award 1848451  and the UCONN IBACS Seed Grants.  Previous supports include research grant from SANOFI, training grant from Traveler and UCONN REP grant.

Publication (Google Scholar Citations)

Submitted Papers

  1. Xiaojing Wang, Abhisek Saha, and Dipak Dey (2024), “Bayesian Joint Modeling of Response Times with Dynamic Latent Ability in Educational Testing”, Psychometrika, revision submitted.
  2. Jingyu Sun, Yang Liu, Xiaojing Wang and Ming-Hui Chen (2024), “Bayesian Variable Selection in Dynamic Item Response Theory Models”, Journal of Educational and Behavioral Statistics, revision submitted.
  3. Jingyu Sun and Xiaojing Wang (2024), “A Novel Finite Mixture Model to Cluster Dynamic Latent Ability in Item Response Theory Models”, Statistics and Its Interface, in revision.
  4. Fang Liu, Ming-Hui Chen, Xiaojing Wang, Roeland Hancock (2024), “Decomposition of WAIC for Assessing the Information Gain with Application to Educational Testing”, British Journal of Mathematical and Statistical Psychology, in revision.
  5. Ganchao Wei, Ian Stevenson and Xiaojing Wang (2024), “A Bayesian Clustering of Subpopulations in Neural Spiking Activity with Dynamic and Flexbile Latent Structures”, arXiv:2205.10639.
  6. Rigel Mahmood, Tan Zhu, Xiaojing Wang, Jinbo Bi (2024), “Learning Microbiome Representation with Covariate-extended Variational Count-Data Autoencoder”, Computers in Biology and Medicine, submitted.
  7. Xiaojing Wang (2024), “Simultaneously Modeling and Detecting Local Dependence in Dynamic Item Response Models”, Journal of Applied Statistics, submitted.

Published Papers

  1. Ganchao Wei, Zeinab Tajik Mansouri, Xiaojing Wang, and Ian Stevenson (2024), “Calibrating Bayesian Decoders of Neural Spiking Activity“, Journal of Neuroscience, 44 (18): e2158232024, DOI: 10.1523/JNEUROSCI.2158-23.2024.
  2. Eduardo Schneider Bueno de Oliveira, Xiaojing Wang and Jorge Luis Bazán (2023), “A Classification Model for Continuous Responses: Identifying Risk Perception Groups on Health-related Activities“, Biometrical Journal, 65(4): 2100222.
  3. Ganchao Wei, Ian Stevenson, and Xiaojing Wang (2022), “Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers“, Advances in Neural Information Processing Systems (NeurIPS), 35: 19499-19510.
  4. Fang Liu, Xiaojing Wang, Roeland Hancock and Ming-Hui Chen (2022), “Bayesian Model Assessment for Jointly Modeling Multidimensional Response Data with Application to Computerized Testing“, Psychometrika, 87(4), 1290-1317.
  5. Panpan Zhang and Xiaojing Wang (2022), “Several Topological Indices of Random Caterpillars”, Methodology and Computing in Applied Probability, 24, 1773-1789.
  6. Tairan Ye, Victor Hugo Lachos Davila, Xiaojing Wang, and Dipak Dey (2020), “Comparisons of zero-inflated continuous regression models from a Bayesian perspective”, Statistics in Medicine, 40 (5): 1073-1100.
  7. Yang Liu, Lijiang Geng, Xiaojing Wang, Donghui Zhang, Ming-Hui Chen (2020), “Subgroup Analysis from Bayesian Perspectives”, in Design and Analysis of Subgroups with Biopharmaceutical Applications, eds. N. Ting, J. C. Cappelleri, S. Ho, and D. D.-G. Chen, pp. 331-345, Springer, Cham.
  8. Yang Liu, Xiaojing Wang  (2020), “Bayesian Nonparametric Monotonic Regression of Dynamic Latent Traits in Item Response Models“,  Journal of Educational and Behavioral Statistics, 45 (3): 274-296.
  9. Abhishek Bishoyi, Xiaojing Wang and Dipak K. Dey (2020), “Learning Semiparametric Regression with Missing Covariates Using Gaussian Process Models“, Bayesian Analysis, 15(1):215–239.
  10. Yang Liu, Lijiang Geng, Xiaojing Wang, Donghui Zhang, Ming-Hui Chen (2020), “Subgroup Analysis from Bayesian Perspectives”, Design and Analysis of Subgroups with Biopharmaceutical Applications, Book Chapter, Page 331-345.
  11. Yang Liu, Guanyu Hu, Lei Cao, Xiaojing Wang, and Ming-Hui Chen (2019), “Rejoinder: A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Responses Theory Models“, Journal of Korean Statistical Society, 48(4):522-523.
  12. Yang Liu, Guanyu Hu, Lei Cao, Xiaojing Wang, and Ming-Hui Chen (2019), “A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Response Theory Models“, Journal of Korean Statistical Society, 48(4):503-512.
  13. Wenling Song, Yanhui Zhao, Shujing Shi, Xianying Liu, Guiying Zheng, Christopher Morosky, Yang Jiao, Xiaojing Wang (2019), “First Trimester Doppler Velocimetry of the Uterine Artery Ipsilateral to the Placenta Improves the Ability to Predict Early Onset Preeclampsia“, Medicine, 98(16): e15193.
  14. Yang Liu, Xiwen Ma, Donghui Zhang, Lijiang Geng, Xiaojing Wang, Wei Zheng and Ming-Hui Chen (2019),“ Look Before You Leap: Systematic Evaluation of Tree-based Statistical Methods in Subgroup Identification“, 29 (6), 1082-1102.
  15. Xiaojing Wang, Yong Zhou, Yang Liu (2018), “Semiparametric Varying-Coefficient Partially Linear Model with Auxiliary Covariates”, Statistics and Its Interface, 4(11): 587-602.
  16. Mengyang Gu, Xiaojing Wang and James O. Berger (2018), “Robust Gaussian Stochastic Process Emulation“, Annals of Statistics, 46(6A): 3038-3066.
  17. Yingying Xie, Adam M. Wilson, and John A. Silander (2018), “Predicting Autumn Phenology: How Deciduous Tree Species Respond to Weather Stressors”, Agricultural and Forest Meteorology, 250-251: 127-137.
  18. Ming-Hui Chen, Wenqing Li, Xiaojing Wang, Dipak K. Dey (2018), “Bayesian Design of Non-Inferiority Clinical Trials via the Bayes Factor”, Statistics in Bioscience, 10(2): 439-459.Zheng Wei, Xiaojing Wang, Erin M. Conlon (2017), “Parallel Computing Methods for Bayesian Dynamic Item Response Models in Educational Testing”, Stat, 6 (1): 420-433.
  19. Xiaojing Wang and James O. Berger (2016), “Estimating Shape Constrained Functions Using Gaussian Processes“, Journal on Uncertainty Quantification, 4(1): 1-25.
  20. Yingying Xie, Xiaojing Wang and John A. Silander (2015), “Autumn Phenology of Deciduous Forest Communities Respond to Temperature, Rainfall Patterns, Drought Implying for Complex Climate Change Impacts”, Proceedings of the National Academy of Sciences of the United States of America, 112 (44): 13585-13590.
  21. Wuqing Wu and Xiaojing Wang (2015), “A Novel Approach to Construct a Composite Indicator by Maximizing Its Sum of Squared Correlations with Sub-indicators“, Journal of Systems Science and Complexity, 28(4): 925-937.
  22. Carlos A. Abanto-Valle, Caifeng Wang, Xiaojing Wang, Fei-Xing Wang and Ming-Hui Chen (2014), “Bayesian Inference for Stochastic Volatility Models Using the Generalized Skew-t Distribution with Applications to the Shenzhen Stock Exchange Returns“, Statistics and Its Interface, 7(4):487-502.
  23. James O. Berger, Xiaojing Wang and Lei Shen, “A Bayesian Approach to Subgroup Identification“, Journal of Biopharmaceutical Statistics, 2014, 24 (1): 110-129.
  24. Xiaojing Wang, James O. Berger and Donald S. Burdick, “Bayesian Analysis of Dynamic Item Response Models in Educational Testing“, Annals of Applied Statistics, 2013, 7 (1): 126-153.
  25. Yong Zhou, Alan T.K. Wan, Shangyu Xie and Xiaojing Wang, “Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance“, Journal of Econometrics, 2010 , 159 (1):183-201.
  26. Yong Zhou, Jinhong You and Xiaojing Wang, “Strong Convergence Rates of Several Estimators in Semiparametric Varying-Coefficient Partially Linear Models“, Acta Mathematica Scientia Series B, English Edition (SCI), 2009, 29(5): 1113-1127.
  27. Yong Zhou, Alan T.K. Wan and Xiaojing Wang, “Estimating Equations Inference with Missing Data“, Journal of the American Statistical Association, 2008, 103(483):1187-1199.
  28. Xiaojing Wang, “The Factor Analysis of and Comprehensive Evaluation on the Development Level of High-Tech Industry in Different Provinces in China”, Mathematics in Practice and Theory, Beijing, 2008, Vol.37 (18):17-28.

Conference Paper

  1. Xiaojing Wang and James O. Berger, “Estimating Shape Constrained Functions Using Gaussian Processes”, Joint Statistical Meeting Proceedings, 2011.

    Paper in Preparation

    1. Zhengkang Liang, Jun S. Liu, Xiaojing Wang and Zhigen Zhao (2024), “Bayesian Analysis of Multiple Index Additive Models”, in revision.
    2. Jingyu Sun and Xiaojing Wang (2024), “Understanding Dynamic Changes of Students’ Abilities with a Shape-based Clustering Method for Tailored Education”.
    3. Yuhao Li and Xiaojing Wang and Dipak K. Dey (2024), “Analyzing Latent Trajectory in Longitudinal Item Responses Using Gaussian Processes”.
    4. Zoe Gibbs McBride and Xiaojing Wang (2024), “Using Gaussian Process Ordinal Regression with Mixture Errors to Understand Student Well-being”.
    5. Zoe Gibbs McBride, Xiaojing Wang, Timothy E. Moore and Erin Mead- Morse (2024), “A Bayesian Joint Hierarchical Modeling Approach to Ana- lyzing Hypothetical Purchase Task Data”.
    6. Eduardo Schneider Bueno de Oliveira, Xiaojing Wang, Jorge Luis Bazán and Jimmy de la Torre (2024), “A Bounded Cognitive Diagnosis Model to Classification Problems”.
    7. Yuhao Li, Xiaojing Wang and Dipak K. Dey (2024), “Objective Bayesian Analysis of Latent Gaussian Process Models”.
    8. Yifan Li, Xiaojing Wang (2024), “Bayesian Analysis of a Flexible Power Link with Item Response Theory Model for Unbalanced Dichotomous Data”.
    9. Lei Cao, Ming-Hui Chen, Cheng Huang, Xiaojing Wang and Roeland Hancock (2024), “Bayesian Joint Pattern-Specific Autoregressive Models for Speedy Response Data with Application to Computerized Testing”.
    10. Panpan Zhang, Xiaojing Wang, W. Hudson Robb, and Dandan Liu (2024), “A Bayesian Approach to Analyzing Networks with Erroneous Links”.
    11. Zhiduo Chen and Xiaojing Wang (2024), “Decomposition of DIC to Evaluate the Necessity for Joint Modeling of Response Times with Cognitive Diagnostic Models”.
    12. Yuhao Li, Xiaojing Wang and Dipak K. Dey (2024), “Modeling a Shape-constrained Latent Trajectory Using Gaussian Processes”.

    Book

    1. Yong Zhou, Shangyu Xie, Xiaojing Wang and Yubei Ma, Translation of “Probability & Statistics for Engineers & Scientists (8th Edition) “, China Machine Press, 2010.

    Ph.D. and M.S. Thesis

    1. Xiaojing Wang (2012), “Bayesian Modeling Using Latent Structures”, 2012, 7.
      Advisor: Professor James O. Berger
    2. Xiaojing Wang (2008), “Statistical Analysis for Semiparametric Varying-coefficient Partially Linear Models with Incomplete Data”, Chinese Master’s Theses Full-text Database, 2008, 7 (9).
      Advisor: Professor Yong Zhou