Publications

Publications/accepted manuscripts

Steele, F., Zhang, S.*, Grundy, E., & Burchardt, T. (2023). Longitudinal analysis of exchanges of support between parents and children in the UK. Journal of the Royal Statistical Society Series A: Statistics in Society, accepted.

Kuha, J., Zhang, S.*, & Steele, F. (2022). Latent variable models for multivariate dyadic data with zero inflation: Analysis of intergenerational exchanges of family support. Annals of Applied Statistics, accepted.

Zhang, S., Chen, Y. (2022). Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework. Psychometrika, 87, 1473–1502.

Chen, Y., Moustaki, I., Zhang, S. (2022). On the Estimation of Structural Equation Models with Latent Variables. In Rick H. Hoyle (ed.), Handbook of Structural Equation Modeling, Second Edition, Guilford Press, 145-162.

Zhang, S., Chen, Y., & Liu, Y. (2020). An improved stochastic EM algorithm for large‐scale full‐information item factor analysis. British Journal of Mathematical and Statistical Psychology, 73, 44–71.
Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. Journal of the American Statistical Association, 1–15.

Chen, Y., & Zhang, S. (2020). A Latent Gaussian process model for analysing intensive longitudinal data. British Journal of Mathematical and Statistical Psychology, 73, 237–260.

Chen, Y., Li, X., & Zhang, S. (2019). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 84, 124–146.

Working on

Zhang, S., Kuha, J., Steele, F. Modelling Correlation Matrices in Multivariate Dyadic Data: Latent Variable Models for Intergenerational Exchanges of Family Support.

Zhang, S., Chen, J., Ying, Z., Zhang, H. Adjusting for non-confounding covariates in case-control association studies.

Zhang, S., Chen, Y. A Note on Ising Network Analysis with Missing Data.