Developing robust methods to handle missing data in real-world applications effectively
It addresses a pervasive challenge in data analysis for researchers and practitioners across various industries, but appears incremental as it builds on prior work on missing data mechanisms.
This PhD project tackles the problem of missing data in real-world applications by focusing on under-explored mechanisms like MAR and MNAR, aiming to develop robust methods to handle them effectively across diverse data types.
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.