XeroGraph: enhancing data integrity in the presence of missing values with statistical and predictive analysis.

ABSTRACT:

Motivation

Missing data present a pervasive challenge in data analysis, potentially biasing outcomes and undermining conclusions if not addressed properly. Missing data are commonly classified into Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). While MCAR poses a minimal risk of data distortion, both MAR and MNAR can seriously affect the results of subsequent analyses. Therefore, it is important to know the type of missing data and appropriately handle them.

Results

To facilitate efficient handling of missing data, we introduce a Python package named XeroGraph that is designed to evaluate data quality, categorize the nature of missingness, and guide imputation decisions. By comparing how various imputation methods influence un

SUBMITTER: Mousafi Alasal L 

PROVIDER: S-EPMC11889451 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature