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

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 efficie...

RUI: Partially Observed Curves, and Big-Data Virtual Bootstrap

The PI will consider problems related to statistical prediction and inference in two challenging setups: (i) There may be censored, partially observed, or missing data, and (ii) the training data size may be very large (the big-data challenge). The focus will be on problems related to statistical classification, regression curve estimation, and density estimation.

RUI: Classification, regression, and density estimation with missing variables

This project develops statistical theory and methods for nonparametric classification and curve estimation in the presence of missing or incomplete data. Many data sets have missing values; these include the data from biomedical studies, remote sensing, as well as social sciences. There are a number of classical approaches for handing the missing data. Many of the existing results first impute for the missing values an...