Xueying Tang

Interests

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Courses

Scholarly Contributions

Journals/Publications

• Zhang, S., Tang, X., Wang, Z., Liu, J., & Ying, Z. (2023). External Correlates of Adult Digital Problem-Solving Process: An Empirical Analysis of PIAAC PSTRE Action Sequences. Zeitschrift fur Psychologie.
• Tang, X. (2023). A latent hidden Markov model for response process data. Psychometrika.
• Tang, X., & Ghosh, M. (2023).

  Global-Local Priors for Spatial Small Area Estimation

  . Calcutta Statistical Association Bulletin. doi:10.1177/00080683231186378
• Tang, X., Wang, Z., Liu, J., & Ying, Z. (2023). Subtask analysis of process data through a predictive model. British Journal of Mathematical and Statistical Psychology, 76(1), 211-235. doi:10.1111/bmsp.12290
• Ghosh, T., Ghosh, M., Maples, J., & Tang, X. (2022). Multivariate global-local priors for small area estimation. Stats, 5(3), 673-688. doi:https://doi.org/10.3390/stats5030040
• Lippitt, W., Lippitt, W., Sethuraman, S., Sethuraman, S., Tang, X., & Tang, X. (2022). Stationarity and Inference in Multistate Promoter Models of Stochastic Gene Expression via Stick-Breaking Measures. SIAM Journal on Applied Mathematics, 82(6), 1953-1986. doi:10.1137/21m1440876
• Tang, X., Wang, Z., Liu, J., & Ying, Z. (2021). An exploratory analysis of the latent structure of process data via action sequence autoencoders. British Journal of Mathematical and Statistical Psychology, 74(1), 1-33. doi:10.1111/bmsp.12203
• Tang, X., Zhang, S., Wang, Z., Liu, J., & Ying, Z. (2021). ProcData: An R Package for Process Data Analysis. Psychometrika.
• Tang, X., Wang, Z., & Liu, J. (2020). Statistical Analysis of Multi-Relational Network Recovery. Frontiers in Applied Mathematics and Statistics.
• Tang, X., Wang, Z., He, Q., Liu, J., & Ying, Z. (2020). Latent feature extraction for process data via multidimensional scaling. Psychometrika.

Presentations

• Tang, X. (2024, April). A Latent Hidden Markov Model for Process Data. Arizona Data Science Day.
• Tang, X. (2024, August). Hidden Markov Cognitive Diagnostic Models for Response Process Data. Joint Statistical Meetings.
• Tang, X. (2024, December).

  A Hierarchical Gamma Prior for Modeling Random Effects in Small Area Estimation.

  . 17th International Conference of ERCIM WG on Computational and Methodological Statistics.
• Tang, X. (2023, April). Modeling Sparsity Using Log-Cauchy Priors. Statistics Seminar at the University of Pittsburgh.
• Tang, X. (2023, August). Adaptive Bayesian Shrinkage of Random Effects in Small Area Estimation. Joint Statistical Meetings. Toronto, Canada.
• Tang, X. (2023, December). Global-Local Priors for Spatial Small Area Estimation. 16th International Conference of the ERCIM WG on Computational and Methodological Statistics.
• Tang, X. (2023, February). A Latent Hidden Markov Model for Response Process Data. Special Interest Group Seminar at ETS.
• Tang, X. (2023, July). A Latent Hidden Markov Model for Response Process Data. International Meeting for Psychometric Society. College Park, Maryland.
• Tang, X. (2023, September). A Latent Hidden Markov Model for Response Process Data. Psychometrics Workshop at Columbia University.
• Tang, X. (2022, April). Modeling sparsity using log Cauchy prior. University of Minnesota Statistics Seminar.
• Tang, X. (2022, April). Subtask analysis of process data through a predictive model. Arizona State University Machine Learning Day.
• Tang, X. (2022, December). Measurement Error Models with Global-Local Random Effects in Small Area Estimation. 15th International Conference of the ERCIM WG on Computational and Methodological Statistics. Online.
• Tang, X. (2022, June). A latent hidden Markov model for response process data. International Chinese Statistician Association Applied Statistics Symposium.
• Tang, X. (2022, October). Modeling sparsity using log-Cauchy prior. Arizona State University Statistical Seminar.
• Tang, X. (2022, October). Modeling sparsity using log-Cauchy prior. University of Cincinnati Statistics Seminar.
• Tang, X. (2021). Using log Cauchy priors for modeling sparsity. 14th International Conference of the ERCIM WG on Computational and Methodological Statistics. Virtual.
• Tang, X. (2021, April). Subtask Analysis of Process Data Through a Predictive Model. The Ohio State University Biostatistics Seminar. Virtual.
• Tang, X. (2021, June). Subtask Analysis of Process Data Through a Predictive Model. University of California Davis Statistics Seminar. Virtual.
• Tang, X. (2021, October). Subtask Analysis of Process Data Through a Predictive Model. 34th New England Statistics Symposium. Virtual.
• Tang, X. (2020, August). Bayesian Semiparametric Regression Model Selection with Correlated Errors. Joint Statistical Meetings.
• Tang, X. (2020, December). Subtask Analysis of Process Data Through a Predictive Model. International Chinese Statistical Association Applied Statistics Symposium.
• Tang, X. (2020, July). A Hidden Markov Model for Identifying Problem Solving Strategies in Process Data. International Meeting of the Psychometric Society.
• Tang, X. (2020, July). Introduction to R package ProcData. Workshop on Statistical Learning for Process Data.