Statistical Inference and Modeling for Complex Data

Case Western Reserve University, Cleveland OH

Investigators

Abstract

This project focuses on three research areas related to complex data. The first research area is on high dimensional graphics, data analysis and related topics. The second research area is on mixture models and bump hunting problems. The third area is on models and inferential procedures for data with biases. Several variants of likelihood, semi-parametric and iterative procedures are sought for modeling and making inferences about the data. We live in the information age. Information often hides in data sets with different appearances. Mining and modeling (massive) data sets include the discovery of patterns, subcomponents, bumps, or special events, and development of models that explain the data and predict the future. This research is aimed at providing solutions to some interesting problems arising from the data which are either high dimension, or complicated, or with a sampling bias. Theories, methods, and efficient computing algorithms are explored for extracting useful information from the complex data. Application areas include astronomy, neurosciences, quality control, and some (other) observational studies.