34 citations · 62 across the 9 of their papers we have counts for
9 papers
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…
Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays
Łukasz Kidziński, Francis K. C. Hui, David I. Warton +1
Unmeasured or latent variables are often the cause of correlations between multivariate measurements, which are studied in a variety of fields such as psychology, ecology, and medi…
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…
Semiparametric Regression using Variational Approximations
Francis K. C. Hui, Chong You, Han Lin Shang +1
Semiparametric regression offers a flexible framework for modeling non-linear relationships between a response and covariates. A prime example are generalized additive models where…
Multi-species distribution modeling using penalized mixture of regressions
Francis K. C. Hui, David I. Warton, Scott D. Foster
Multi-species distribution modeling, which relates the occurrence of multiple species to environmental variables, is an important tool used by ecologists for both predicting the di…
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…