3 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
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 Misspecification and its Consequences for Prediction in Generalized Linear Mixed Models
Quan Vu, Francis K. C. Hui, Samuel Muller +1
When fitting generalized linear mixed models (GLMMs), one important decision to make relates to the choice of the random effects distribution. As the random effects are unobserved,…
On the effect of noise on fitting linear regression models
Insha Ullah, A. H. Welsh
In this study, we explore the effects of including noise predictors and noise observations when fitting linear regression models. We present empirical and theoretical results that…
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…
Asymptotic Results for Penalized Quasi-Likelihood Estimation in Generalized Linear Mixed Models
Xu Ning, Francis Hui, Alan Welsh
Generalized Linear Mixed Models (GLMMs) are widely used for analysing clustered data. One well-established method of overcoming the integral in the marginal likelihood function for…
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…