Handling high-dimensional data with missing values by modern machine learning techniques

Abstract

High-dimensional data have been regarded as one of the most important types of big data in practice. It happens frequently in practice including genetic study, financial study, and geographical study. Missing data in high dimensional data analysis should be handled properly to reduce nonresponse bias. We discuss some modern machine learning techniques including penalized regression approaches, tree-based approaches, and deep learning (DL) for handling missing data with high dimensionality. Specifically, our proposed methods can be used for estimating general parameters of interest including population means and percentiles with imputation-based estimators, propensity score estimators, and doubly robust estimators. We compare those methods through some limited simulation studies and a real application. Both simulation studies and real application show the benefits of DL and XGboost approaches compared with other methods in terms of balancing bias and variance.

Keywords:

• Deep learning
• high-dimensional data
• imputation
• machine learning
• missing data

Acknowledgments

Dr Sixia Chen is partly supported by the National Institute on Minority Health and Health Disparities at National Institutes of Health (1R21MD014658-01A1) and the Oklahoma Shared Clinical and Translational Resources (U54GM104938) with an Institutional Development Award (IDeA) from National Institute of General Medical Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Part of the computing for this project was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Dr Sixia Chen is partly supported by the National Institute on Minority Health and Health Disparities at National Institutes of Health (1R21MD014658-01A1) and the Oklahoma Shared Clinical and Translational Resources (U54GM104938) with an Institutional Development Award (IDeA) from National Institute of General Medical Sciences.