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20212025
most citedAsymptotic Results for Penalized Quasi-Likelihood Estimation in Generalized Linear Mixed Models

3 citations · 3 across the 7 of their papers we have counts for

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9 papers

stat.AP2025

A Single Index Approach to Integrated Species Distribution Modeling for Fisheries Abundance Data

Quan Vu, Francis K. C. Hui, A. H. Welsh +3

In fisheries ecology, species abundance data are often collected by multiple surveys, each with unique characteristics. This article is motivated by a dataset of Atlantic sea scall…

stat.ME2024

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…

stat.ME2024

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,…

math.ST2024

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…

stat.ME2024

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…

math.ST2024★ 3 cited

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…