3 papers
stat.ML2023
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…
stat.ME2022
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…
stat.AP2020
Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19
Anatoly Zhigljavsky, Roger Whitaker, Ivan Fesenko +10
Coronavirus COVID-19 spreads through the population mostly based on social contact. To gauge the potential for widespread contagion, to cope with associated uncertainty and to info…