Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems
Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GAN leverages recent progress in conditional generative adversarial neural works and shows strong performance matching existing state-of-the-art imputation methods on high-dimensional datasets, in terms of imputation error. In particular, MI-GAN significantly outperforms other imputation methods in the sense of statistical inference and computational speed.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkImputationSimilar Papers 제목 키워드 기반
GAMIN: Generative Adversarial Multiple Imputation Network for Highly Missing Data
We propose a novel imputation method for highly missing data. Though most existing imputation methods focus on moderate missing rate, imputation for high missing rate over 80% is still important but challenging. As we ex…
Generative Adversarial NetworkImputationMissing value imputation with adversarial random forests -- MissARF
Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with advers…
Density EstimationImputationMissing ValuesIGANI: Iterative Generative Adversarial Networks for Imputation with Application to Traffic Data
Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data. As one of the effective imputati…
ImputationManagementTraffic PredictionDifferentiable and Scalable Generative Adversarial Models for Data Imputation
Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications…
ImputationClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)
Many studies have attempted to solve the problem of missing data using various approaches. Among them, Generative Adversarial Imputation Nets (GAIN) was first used to impute data with Generative Adversarial Nets (GAN) an…
ImputationTransfer Learning