34 citations · 62 across the 9 of their papers we have counts for
6 papers · 1 filter
Fast, universal estimation of latent variable models using extended variational approximations
Pekka Korhonen, Francis K. C. Hui, Jenni Niku +1
Generalized linear latent variable models (GLLVMs) are a class of methods for analyzing multi-response data which has garnered considerable popularity in recent years, for example,…
Sparse Sliced Inverse Regression via Cholesky Matrix Penalization
Linh Nghiem, Francis K. C. Hui, Samuel Mueller +1
We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization and its adaptive version for achieving sparsity in estimating the dimensions of th…
Screening methods for linear errors-in-variables models in high dimensions
Linh Nghiem, Francis K. C. Hui, Samuel Mueller +1
Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a smal…
Symbolic Formulae for Linear Mixed Models
Emi Tanaka, Francis K. C. Hui
A statistical model is a mathematical representation of an often simplified or idealised data-generating process. In this paper, we focus on a particular type of statistical model,…
Bootstrapping F test for testing Random Effects in Linear Mixed Models
P. Y. O'Shaughnessy, Francis Hui, Samuel Muller +1
Recently Hui et al. (2018) use F tests for testing a subset of random effect, demonstrating its computational simplicity and exactness when the first two moment of the random effec…
A Nonparametric Measure of Local Association for two-way Contingency Tables
Francis K. C. Hui, Gery Geenens
In contingency table analysis, the odds ratio is a commonly applied measure used to summarize the degree of association between two categorical variables, say R and S. Suppose now…