research
∙
07/05/2023
A Complete Characterisation of Structured Missingness
Our capacity to process large complex data sources is ever-increasing, p...
share
research
∙
04/04/2023
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning ta...
research
∙
11/21/2022
A Framework for Auditable Synthetic Data Generation
Synthetic data has gained significant momentum thanks to sophisticated m...
research
∙
08/15/2022
An integrated approach to test for missingness not at random
Missing data can lead to inefficiencies and biases in analyses, in parti...
research
∙
05/12/2022
On integrating the number of synthetic data sets m into the 'a priori' synthesis approach
Until recently, multiple synthetic data sets were always released to ana...
research
∙
11/15/2021
Bayesian modelling and computation utilising cycles in multiple network data
Modelling multiple network data is crucial for addressing a wide range o...
research
∙
07/16/2021
Moving towards practical user-friendly synthesis: Scalable synthetic data methods for large confidential administrative databases using saturated count models
Over the past three decades, synthetic data methods for statistical disc...
research
∙
07/07/2021
