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07/05/2023
A Complete Characterisation of Structured Missingness
Our capacity to process large complex data sources is ever-increasing, p...
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04/04/2023
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning ta...
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11/21/2022
A Framework for Auditable Synthetic Data Generation
Synthetic data has gained significant momentum thanks to sophisticated m...
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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...
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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...
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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...
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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...
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07/07/2021