4 citations · 8 across the 7 of their papers we have counts for
10 papers · 1 filter
Structural randomised selection
Fan Wang, Sylvia Richardson, Steven M. Hill
An important problem in the analysis of high-dimensional omics data is to identify subsets of molecular variables that are associated with a phenotype of interest. This requires ad…
Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables
Anaïs Rouanet, Rob Johnson, Magdalena E Strauss +4
The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelli…
Interoperability of statistical models in pandemic preparedness: principles and reality
George Nicholson, Marta Blangiardo, Mark Briers +12
We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving p…
Kernel learning approaches for summarising and combining posterior similarity matrices
Alessandra Cabassi, Sylvia Richardson, Paul D. W. Kirk
When using Markov chain Monte Carlo (MCMC) algorithms to perform inference for Bayesian clustering models, such as mixture models, the output is typically a sample of clusterings (…
Shrinkage estimation of large covariance matrices using multiple shrinkage targets
Harry Gray, Gwenaël G. R. Leday, Catalina A. Vallejos +1
Linear shrinkage estimators of a covariance matrix --- defined by a weighted average of the sample covariance matrix and a pre-specified shrinkage target matrix --- are popular whe…
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
Fan Wang, Sach Mukherjee, Sylvia Richardson +1
Penalized likelihood approaches are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well-developed, the relat…