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 REP grant. Previous supports include research grant from SANOFI and training grant from Traveler.

 

Publication (Google Scholar Citations)

Submitted Papers

  1. Xiaojing Wang, Abhisek Saha, and Dipak Dey (2023), “Bayesian Analysis of Joint Modeling Response Times with Dynamic Latent Ability in Educational Testing”, submitted to Psychometrika.
  2. Wuqing Wu, Xiaojing Wang, Muzi Chen (2017), “Statistical Modeling of Earnings Distribution: Properties of Estimators, Simulation and Empirical Evidence”, submitted to Journal of Applied Statistics, in revision.
  3. Tairan Ye, Victor Hugo Lachos Davila, Xiaojing Wang, and Dipak Dey (2019), “Bayesian Analysis of Tweedie Model with Conditional Autoregressive Prior”, The Journal of the International Actuarial Association.
  4. Fang Liu, Xiaojing Wang, Roeland Hancock and Ming-Hui Chen (2020), “Bayesian Joint Item Response Model of Multidimensional Response Data with Application to Computerized Testing”, submitted to Psychometrika, in revision.

Published Papers

  1. 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, in press.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Xiaojing Wang, Yong Zhou, Yang Liu (2018), “Semiparametric Varying-Coefficient Partially Linear Model with Auxiliary Covariates”, Statistics and Its Interface, 4(11): 587-602.
  10. Mengyang Gu, Xiaojing Wang and James O. Berger (2018), “Robust Gaussian Stochastic Process Emulation“, Annals of Statistics, 46(6A): 3038-3066.
  11. 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.
  12. 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.
  13. 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.
  14. Xiaojing Wang and James O. Berger (2016), “Estimating Shape Constrained Functions Using Gaussian Processes“, Journal on Uncertainty Quantification, 4(1): 1-25.
  15. 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.
  16. 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.
  17. 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.
  18. James O. Berger, Xiaojing Wang and Lei Shen, “A Bayesian Approach to Subgroup Identification“, Journal of Biopharmaceutical Statistics, 2014, 24 (1): 110-129.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.

Technical Reports

  1. Yang Liu, Lijiang Geng, Xiaojing Wang, Ming-Hui Chen (2019), “Subgroup Analysis from Bayesian Perspectives”, Technical Reports, Department of Statistics, University of Connecticut, 2019-06.
  2. Yang Liu, Xiwen Ma, Donghui Zhang, Lijiang Geng, Xiaojing Wang, Wei Zheng and Ming-Hui Chen (2017), “Look Before You Leap: Systematic Evaluation of Tree-based Statistical Methods in Subgroup Identification”, Technical Reports, Department of Statistics, University of Connecticut, 2017-09.
  3. Xiaojing Wang, Abhisek Saha, and Dipak Dey (2016), “Bayesian Analysis of Joint Modeling Response Times with Dynamic Latent Ability in Educational Testing”, Technical Reports, Department of Statistics, University of Connecticut, 2016-03.
  4. Richard Huggins, Xiaojing Wang and Yong Zhou (2014), “Semiparametric Estimation of Animal Abundance Using Capture-recapture Data from Open Populations: Log-Linear models”, Technical Reports, Department of Statistics, University of Connecticut, 2014-23.
  5. Ioanna Manolopoulou, Xiaojing Wang, Chunlin Ji, Heather E. Lynch, Shelley Stewart, Gregory D. Sempowski, S. Munir Alam, Mike West and Thomas B. Kepler, “Statistical Analysis of Cellular Aggregates in Immunofluorescence Histology“, Discussion Paper, the Department of Statistical Science, Duke University, 2009-19.

Paper in Preparation

  1. Wuqing Wu, Xiaojing Wang (2018), “Detecting Earnings Management: A Novel Tobit Modeling Approach”.
  2. Abhishek Bishoyi, Xiaojing Wang and Dipak K. Dey (2018), “Flexible Symmetric Power Link Functions in Nonparametric Ordinal Regression with Gaussian Process Priors”.
  3. Xiaojing Wang (2018), “Bayesian Clustering Students’ Ability Trajectories in Dynamic Item Response Models”.
  4. Zheng Wei, Xiaojing Wang, Erin M. Conlon (2018), “Parallel Markov Chain Monte Carlo for Bayesian Dynamic Item Response Models in Educational Testing, in Two Stages”.
  5. Abhishek Bishoyi, Xiaojing Wang and Dipak K. Dey (2018), “Modeling Group Sparsity with Gaussian Process Priors”.
  6. Eduardo Schneider, Xiaojing Wang and Jorge Bazán (2019), “Bayesian Analysis of Cognitive Diagnosis Models for Continuous Responses”.

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