Selected Publications
A complete list of my publications is available in my CV.
Statistical Inference
Liu, Y., Hannig, J., & Murph, A. C. (2025). A geometric perspective on Bayesian and generalized fiducial inference. Statistical Science, 40(2) 219-234. https://doi.org/10.1214/24-STS928
Williams, J. P., & Liu, Y. (2024). Decision theory via model-free generalized fiducial inference. In Bi, Y., Jousselme, AL., Denoeux, T. (eds), Belief Functions: Theory and Applications. BELIEF 2024. Springer, Cham. https://doi.org/10.1007/978-3-031-67977-3_14
Liu, Y., & Sweet T. M. (2023). Statistical inference: Bayesian approaches. In Tierney, R., Rizvi, F., and Ercikan, K. (Eds.), International Encyclopedia of Education, 4th Edition. Oxford: Elsevier Ltd.
Liu, Y., Hannig, J., & Pal Majumder, A. (2019). Second-order probability matching priors for the person parameter in unidimensional item response theory models. Psychometrika, 84(3), 529–553. https://doi.org/10.1007/s11336-019-09675-4
Chalmers, R. P., Pek, J., & Liu, Y. (2017). Profile-likelihood confidence intervals in item response theory models. Multivariate Behavioral Research, 52(5), 533–550. https://doi.org/10.1080/00273171.2017.1329082
Liu, Y. & Hannig, J. (2017). Generalized fiducial inference for logistic graded response models. Psychometrika, 82(4), 1097–1125. https://doi.org/10.1007/s11336-017-9554-0
Liu, Y., & Hannig, J. (2016). Generalized fiducial inference for binary logistic item response models. Psychometrika, 81(2), 290–324. https://doi.org/10.1007/s11336-015-9492-7
Reliability and Measurement Precision
Sung, Y., & Liu, Y. (2025). Asymptotic standard errors for reliability coefficients in item response theory. https://arxiv.org/abs/2503.22924
Liu, Y., Pek, J., & Maydeu-Olivares, A. (2025). Understanding measurement precision from a regression perspective. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000763
Liu, Y., &, Pek, J., & Maydeu-Olivares, A. (2025). On a general theoretical framework of reliability. British Journal of Mathematical and Statistical Psychology, 78(1), 286-302. https://doi.org/10.1111/bmsp.12360
Liu, Y., & Pek, J. (2024). Summed versus estimated factor scores: Considering uncertainties when using observed scores. Psychological Methods. Advance Online Publication. https://doi.org/10.1037/met0000644
Liu, Y. & Yang, J. S. (2018). Bootstrap-calibrated interval estimates for latent variable scores in item response theory. Psychometrika, 83(2), 333-354. https://doi.org/10.1007/s11336-017-9582-9
Model Fit Assessment
Sung, Y., Han, Y., & Liu, Y. (2025). A new fit assessment framework for common factor models using generalized residuals. Psychometrika. Advance Online Publication. https://doi.org/10.1017/psy.2025.10037
Han, Y., Liu, Y., & Yang, J. S. (2024). Assessing item fit using expected score curve under restricted recalibration. Journal of Educational and Behavioral Statistics. Advance Online Publication. https://doi.org/10.3102/10769986241268604
Wang, W., Liu, Y., & Liu, H. (2022). Testing differential item functioning without predefined anchor items using robust regression. Journal of Educational and Behavioral Statistics, 47(6), 666-692. https://doi.org/10.3102/10769986221109208
Liu, Y., Yang, J. S., & Maydeu-Olivares, A. (2019). Restricted recalibrations of item response theory models. Psychometrika, 84(2), 529–553. https://doi.org/10.1007/s11336-019-09667-4
Liu, Y., & Maydeu-Olivares, A. (2014). Identifying the source of misfit in item response theory models. Multivariate Behavioral Research, 49(4), 354–371. https://doi.org/10.1080/00273171.2014.910744
Liu, Y. & Thissen, D. (2014). Comparing score tests and other local dependence diagnostics for the graded response model. British Journal of Mathematical and Statistical Psychology, 67(3), 496–513. https://doi.org/10.1111/bmsp.12030
Parameter Estimation
Luo, X., Liu, H., Hu, Y., & Liu, Y. (2025). Enhancing two-stage estimation in differential equation models: A bias correction method via stochastic approximation. https://osf.io/preprints/psyarxiv/g8rjt_v1
Liu, Y. (2021). Riemannian Newton and trust-region algorithms for analytic rotation in exploratory factor analysis. British Journal of Mathematical and Statistical Psychology, 74(1), 139-163. https://doi.org/10.1111/bmsp.12211
Liu, Y. (2020). A Riemannian optimization algorithm for joint maximum likelihood estimation of high-dimensional exploratory item factor analysis. Psychometrika, 85(2), 439-468. https://doi.org/10.1007/s11336-020-09711-8
Latent Variable Modeling
Morell, M., Kwon, M., Han, Y., Sung, Y., Liu, Y., & Yang, J. (in press). Regression discontinuity analysis with latent variables. Multivariate Behavioral Research.
Liu, Y., & Wang, W. (2024). What can we learn from a semiparametric factor analysis of item responses and response time? An illustration with the PISA 2015 data. Psychometrika, 89(2), 386-410. https://doi.org/10.1007/s11336-023-09936-3
Liu, Y., & Wang, W. (2022). Semiparametric factor analysis for item-level response time data. Psychometrika, 87(2), 666-692. https://doi.org/10.1007/s11336-021-09832-8
Magnus, B., & Liu, Y. (2018). A zero-inflated Box-Cox normal unipolar item response model for measuring constructs of psychopathology. Applied Psychological Measurement, 42(7), 571–589. https://doi.org/10.1177/0146621618758291
Liu, Y., Magnus, B. E., & Thissen, D. (2016). Modeling and testing differential item functioning in unidimensional binary item response models with a single continuous covariate: A functional data analysis approach. Psychometrika, 81(2), 371–398. https://doi.org/10.1007/s11336-015-9473-x
Applications
Magnus, B. E., & Liu, Y. (2022). Symptom presence and symptom severity as unique indicators of psychopathology: An application of multidimensional zero-inflated and hurdle graded response models. Educational and Psychological Measurement, 82(5), 938-966. https://doi.org/10.1177/00131644211061820
Mereish, E. H., Miranda, R., Jr., Liu, Y., & Hawthorne, D. J. (2021). A daily diary study of minority stress and negative and positive affect among racially diverse sexual minority adolescents. Journal of Counseling Psychology, 68(6), 670–681. https://doi.org/10.1037/cou0000556