4 papers
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…
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…
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…
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…