We are pleased to announce that Alexander Giessing, Acting Assistant Professor of Statistics, has received grant funding support from the National Science Foundation for his research on “Semiparametric Efficient and Robust Inference on High-Dimensional Data”.
The project aims to develop new methodology for inference on high-dimensional and incomplete data. Most data collected for scientific or industrial purposes lack information about certain features that were lost at the sampling stage due to factors such as experimental design, non-compliance, or technical problems. This issue is particularly prevalent in high-dimensional data sets, where each observation comprises many features. Failing to effectively address this issue results in inefficient and biased inference and can lead to spurious scientific discoveries.
This project will develop two complementary lines of research: first, a novel framework for semiparametric efficient inference in high dimensions after having adjusted for incomplete data and, second, new multiplier bootstrap tests for simultaneous and large-scale multiple testing problems that are robust to missingness.
Congratulations to Alexander Giessing on his success!