Representative Publications

Selected Publications

* equal contribution; # my student

Zhang, X. & Wu. H. (online) Investigating structural model fit evaluation.  Structural Equation Modeling.

Du, H. & Wu. H. (online) Estimating the weight matrix in distributionally weighted least squares estimation: An empirical Bayesian solution. Structural Equation Modeling.

Shinn, M., Yu, H., Zoltowski, A. R. & Wu. H. (online) Learning more from homeless point-in-time counts. Housing Policy Debate

Cho, S.-J.*, Wu. H.* & Naveiras (2024) The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model. British Journal of Mathematical and Statistical Psychology, 77, 289-315

Liu, H., Qu, W., Zhang, Z. & Wu, H. (2022) A new Bayesian structural equation modeling approach with priors on the covariance matrix parameter. Journal of Behavioral Data Science, 2(2), 23-46

Gu, F., Wu, H., Yung, Y. -F., & Wilkins, J. L. M. (2021) Standard error estimates for rotated estimates of canonical correlation analysis: an implementation of the infinitesimal jackknife method. Behaviormetrika,48, 143–168.

Lalor, J. P., Wu, H. & Yu, H. (2019). Learning latent parameters without human response patterns: Item response theory with artificial crowds. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 4240–4250.

Lalor, J. P., Wu, H., Munkhdalai, T. & Yu, H. (2018). Understanding deep learning performance through an examination of test set difficulty: A psychometric case study. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 4711–4716.

Lalor, J. P., Wu, H., Chen, L., Mazur, K. & Yu, H. (2018). ComprehENotes: An instrument for assessing patient EHR note reading comprehension: development and validation. Journal of Medical Internet Research, 20(4):e139. doi:10.2196/jmir.9380

Gu, F & Wu, H.(2018). Simultaneous canonical correlation analysis with invariant canonical loadings. Behaviormetrika, 45(1),111-132. https://doi.org/10.1007/s41237-017-0042-8

Wu, H. (2018). Approximations to the distribution of test statistic in covariance structure analysis: a comprehensive study, British Journal of Mathematical and Statistical Psychology, 71, 334-362

Pek, J.* & Wu, H.* (2018). Parameter uncertainty in structural equations models: Confidence sets and fungible estimates, Psychological Methods, 23(4), 635–653

Cheng, C# & Wu, H. (2017). Confidence intervals of fit indexes by inverting a bootstrap test, Structural Equation Modeling, 24(6), 870-880.

Wu, H. (2016) A note on the identifiability of fixed effect 3PL models. Psychometrika, 81(4), 1093-1097

Wu, H. & Estabrook, C. R. (2016) Identification of CFA models of different levels of invariance for ordered categorical outcomes. Psychometrika, 81(4), 1014-1045

Lalor, J. P., Wu, H., & Yu, H. (2016). Building an Evaluation Scale using Item Response Theory. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 648–657.

Gu, F. & Wu, H.(2016). Raw data maximum likelihood estimation for principal component analysis and two types of common principal component model: A state space approach. Psychometrika,81(3), 751-773

Wu, H. & Lin, J. (2016) A Scaled F-distribution as Approximation to the Distribution of Test Statistic in Covariance Structure Analysis, Structural Equation Modeling, 23(3), 409-421

Pek, J. & Wu, H. (2015). Profile likelihood-based confidence regions for structural equation models. Psychometrika, 80(4), 1123-1145

Wu, H. & Browne, M. W. (2015b) Random model discrepancy: Interpretation and technicalities (a rejoinder). Psychometrika, 80(3), 619-624

Wu, H. & Browne, M. W. (2015a) Quantifying adventitious error in a covariance structure as a random effect. Psychometrika, 80(3), 571-600

Dong, L.*, Wu, H.* & Waldman, I. (2014) Measurement and structural invariance of the antisocial process screening device. Psychological Assessment, 26(2), 598-608

Wu, H. & Neale, M. C. (2013). On the likelihood ratio tests in bivariate ACDE models. Psychometrika, 78(3), 441-463

Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior Genetics, 42, 886-898

Wu, H., Myung, I. J. & Batchelder, W. H. (2010a). Minimum description length model selection of multinomial processing tree models. Psychonomic Bulletin and Review, 17, 275-286

Wu, H., Myung, I. J. & Batchelder, W. H. (2010b). On the complexity of multinomial processing tree models. Journal of Mathematical Psychology, 54, 291–303