Developing robust methods to handle missing data in real-world applications effectively

TL;DR

This paper addresses the challenge of missing data across various data types and mechanisms, aiming to develop robust methods that effectively handle MCAR, MAR, and MNAR scenarios for practical applications.

Contribution

It proposes a comprehensive research agenda to investigate and develop methods that accommodate different missing data mechanisms, filling gaps in current approaches.

Findings

Addresses missing data in multiple data modalities

Focuses on all major missing data mechanisms (MCAR, MAR, MNAR)

Aims to provide practical solutions for real-world datasets

Abstract

Missing data is a pervasive challenge spanning diverse data types, including tabular, sensor data, time-series, images and so on. Its origins are multifaceted, resulting in various missing mechanisms. Prior research in this field has predominantly revolved around the assumption of the Missing Completely At Random (MCAR) mechanism. However, Missing At Random (MAR) and Missing Not At Random (MNAR) mechanisms, though equally prevalent, have often remained underexplored despite their significant influence. This PhD project presents a comprehensive research agenda designed to investigate the implications of diverse missing data mechanisms. The principal aim is to devise robust methodologies capable of effectively handling missing data while accommodating the unique characteristics of MCAR, MAR, and MNAR mechanisms. By addressing these gaps, this research contributes to an enriched understanding of the challenges posed by missing data across various industries and data modalities. It seeks to provide practical solutions that enable the effective management of missing data, empowering researchers and practitioners to leverage incomplete datasets confidently.