Bloomberg L.P.

Imputation of missing ESG data using deep latent variable models

In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...
Nature

Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks

Feed-forward neural networks (FFNNs) have received considerable attention due to their successful use in a wide variety of statistical applications, including regression and classification problems. A ...
IEEE

Multi-dimensional Attention-based GAIN for Missing Data Imputation in Multimode Batch Processes

Abstract: In batch processes, due to sensor failure, unstable signal transmission and human negligence, the collected process data often contains missing values. This leads to the decline of process ...
IEEE

Multidimensional Attention-Based GAIN for Missing Data Imputation in Multimode Batch Processes

Abstract: In batch processes, due to sensor failure, unstable signal transmission, and human negligence, the collected process data often contains missing values. This leads to the decline of process ...
unece

Expert Meeting on Statistical Data Editing

This meeting was organized as part of the Conference of European Statisticians’ work programme for 2022, within the context of the High-Level Group for the Modernisation of Official Statistics. In ...