1 citations · 1 across the 7 of their papers we have counts for
8 papers
A Complete Characterisation of Structured Missingness
James Jackson, Robin Mitra, Niels Hagenbuch +2
Our capacity to process large complex data sources is ever-increasing, providing us with new, important applied research questions to address, such as how to handle missing values…
Learning from data with structured missingness
Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti +17
Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. H…
A Framework for Auditable Synthetic Data Generation
Florimond Houssiau, Samuel N. Cohen, Lukasz Szpruch +5
Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that enable the synthesis of high-dimensional datasets. However, many generation techn…
An integrated approach to test for missing not at random
Jack Noonan, Adetola Adedamola Adediran, Robin Mitra +1
Missing data can lead to inefficiencies and biases in analyses, in particular when data are missing not at random (MNAR). It is thus vital to understand and correctly identify the…
On integrating the number of synthetic data sets into the 'a priori' synthesis approach
James Edward Jackson, Robin Mitra, Brian Joseph Francis +1
Until recently, multiple synthetic data sets were always released to analysts, to allow valid inferences to be obtained. However, under certain conditions - including when saturate…
Using saturated count models for user-friendly synthesis of categorical data
James Edward Jackson, Robin Mitra, Brian Joseph Francis +1
Over the past three decades, synthetic data methods for statistical disclosure control have continually evolved, but mainly within the domain of survey data sets. There are certain…
Bayesian model-based clustering for populations of network data
Anastasia Mantziou, Simon Lunagomez, Robin Mitra
There is increasing appetite for analysing populations of network data due to the fast-growing body of applications demanding such methods. While methods exist to provide readily i…
On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression
Joyee Ghosh, Yingbo Li, Robin Mitra
In logistic regression, separation occurs when a linear combination of the predictors can perfectly classify part or all of the observations in the sample, and as a result, finite…