Predictive models with Incomplete and Fragmented Observations, and New Advances in Virtual Re-sampling for Big Data

Project Type:

Project

Project Sponsors:

National Science Foundation - NSF

Project Award:

$200,000

Project Timeline:

2023-09-01 – 2026-08-31

Lead Principal Investigator:

Many real-world data sets involve censored or missing portions and that makes the task of prediction and inference more complicated. Part of this research project focuses on the development of new flexible statistical methods to perform accurate prediction and inference in the presence of incomplete data. Another part of this research project considers the development of new efficient re-sampling methods to deal with Big-data scenarios, where the data size may be too large to invoke classical approaches.