Conditional expectation with regularization for missing data imputation

TL;DR

This paper introduces DIMV, a scalable, explainable, and effective imputation method for missing data that achieves low RMSE and provides confidence regions, suitable for critical applications like medicine and finance.

Contribution

The paper proposes DIMV, a novel regularized conditional distribution-based imputation algorithm that outperforms state-of-the-art methods in accuracy, scalability, and explainability.

Findings

DIMV achieves lower RMSE than existing methods.

DIMV is fast, scalable, and explainable.

Provides confidence regions for imputed values.

Abstract

Missing data frequently occurs in datasets across various domains, such as medicine, sports, and finance. In many cases, to enable proper and reliable analyses of such data, the missing values are often imputed, and it is necessary that the method used has a low root mean square error (RMSE) between the imputed and the true values. In addition, for some critical applications, it is also often a requirement that the imputation method is scalable and the logic behind the imputation is explainable, which is especially difficult for complex methods that are, for example, based on deep learning. Based on these considerations, we propose a new algorithm named "conditional Distribution-based Imputation of Missing Values with Regularization" (DIMV). DIMV operates by determining the conditional distribution of a feature that has missing entries, using the information from the fully observed features as a basis. As will be illustrated via experiments in the paper, DIMV (i) gives a low RMSE for the imputed values compared to state-of-the-art methods; (ii) fast and scalable; (iii) is explainable as coefficients in a regression model, allowing reliable and trustable analysis, makes it a suitable choice for critical domains where understanding is important such as in medical fields, finance, etc; (iv) can provide an approximated confidence region for the missing values in a given sample; (v) suitable for both small and large scale data; (vi) in many scenarios, does not require a huge number of parameters as deep learning approaches; (vii) handle multicollinearity in imputation effectively; and (viii) is robust to the normally distributed assumption that its theoretical grounds rely on.