Sophia Rabe-Hesketh is a statistician conducting methodological research in multilevel and latent variable modeling. She has developed a modeling framework called GLLAMM (Generalized Linear Latent and Mixed Modeling) and written a publicly available software package called gllamm (http://www.gllamm.org/) to estimate these models. The theory of these models is published in Generalized Latent Variable Modeling, co-authored with Anders Skrondal. She has developed approximate methods for maximum likelihood estimation of models with high-dimensional latent variables and is currently doing research on handling missing data. Her papers are published in Psychometrika, Journal of Econometrics, Biometrics, and Journal of the Royal Statistical Society, Series A, among others. Sophia Rabe-Hesketh is also a member of the Interdepartmental Group in Biostatistics.
Research Expertise and Interest
biostatistics, educational statistics, latent variable models, multilevel models, generalized linear latent and mixed models, hierarchical models, longitudinal data, Item response models, structural equation models