Linear mixed effect models are powerful tools used to account for population structure in
genome-wide association studies (GWASs) and estimate the genetic architecture of complex …

Mediation analysis has been a popular framework for elucidating the mediating mechanism
of the exposure effect on the outcome in many disciplines including genetic studies …

Identifying spatial clusters of different regression coefficients is a useful tool for discerning
the distinctive relationship between a response and covariates in space. Most of the existing …

Many scientific and engineering challenges—ranging from pharmacokinetic drug dosage
allocation and personalized medicine to marketing mix (4Ps) recommendations—require an …

Background Gene set analysis is a popular approach to examine the association between a
predefined gene set and a phenotype. Few methods have been developed for a continuous …

Large crossed mixed effects models with imbalanced structures and missing data pose
major computational challenges for standard Bayesian posterior sampling algorithms, as the …

Hierarchical modelling can be applied to data organised in groups, for which we are
interested in describing the within and between group variability. This type of model is very …

In this dissertation, we look at three problems in applied statistics and machine learning. The
first chapter considers the problem of fitting deeply nested hierarchical linear mixed models …

Improving disease outcome prediction can greatly aid in the strategic deployment of
secondary prevention approaches. We develop two methods to predict the evolution of …

We consider a particular instance of a common problem in recommender systems, using a
database of book reviews to inform user-targeted recommendations. In our dataset, books …