6 papers
Finite Mixtures of Generalized Estimating Equations for Clustering Multivariate Correlated Outcomes
Shonosuke Sugasawa, Francis K. C. Hui
Multivariate correlated outcomes occur across disciplines, including ecology, social sciences, and psychometrics. This paper focuses on clustering these outcomes across observation…
High-dimensional sliced inverse regression with endogeneity
Linh H. Nghiem, Francis. K. C. Hui, Samuel Muller +1
Sliced inverse regression (SIR) is a popular sufficient dimension reduction method that identifies a few linear transformations of the covariates without losing regression informat…
Random effects model-based sufficient dimension reduction for independent clustered data
Linh H. Nghiem, F. K. C. Hui
Sufficient dimension reduction (SDR) is a popular class of regression methods which aim to find a small number of linear combinations of covariates that capture all the information…
Variational Bayes for Mixture of Gaussian Structural Equation Models
Khue-Dung Dang, Luca Maestrini, Francis K. C. Hui
Structural equation models (SEMs) are commonly used to study the structural relationship between observed variables and latent constructs. Recently, Bayesian fitting procedures for…
Robust Linear Mixed Models using Hierarchical Gamma-Divergence
Shonosuke Sugasawa, Francis K. C. Hui, Alan H. Welsh
Linear mixed models (LMMs) are a popular class of methods for analyzing longitudinal and clustered data. However, such models can be sensitive to outliers, and this can lead to bia…
Restricted maximum likelihood estimation in generalized linear mixed models
Luca Maestrini, Francis K. C. Hui, Alan H. Welsh
Restricted maximum likelihood (REML) estimation is a widely accepted and frequently used method for fitting linear mixed models, with its principal advantage being that it produces…