Inference Based on Pairwise Distance/Dissimilarity Measures

Ohio State University, The, Columbus OH

Investigators

Abstract

With developments in modern information technology, massive datasets with complicated structures have been collected in many scientific fields such as astronomy, biology, climatology, etc. In this project, the investigator plans to explore the connections between the data sampling distribution and spectrum of the distribution dependent operators and to develop a theoretical foundation for analyzing commonly used spectral techniques based on pairwise distance/dissimilarity measures. Based on the theoretical analysis, a new class of statistical inference tools will be proposed for robust estimation, dimension reduction, clustering and data summarization. Computationally effective algorithms will be designed and their software implementations will be disseminated. Besides theoretical development in statistical methodology, the proposed inference tools will be applied to climate change studies using satellite data and climate model outputs. The proposed research is motivated by real world scientific problems that require statistical inference from massive datasets. The proposed method is designed to extract useful information and knowledge from those massive datasets with complicated structures. The novel algorithms to be developed in this project have the potential to not only help geoscientists and climate modelers in analyzing climate records and calibrating climate models, but also provide statistical tools for scientific investigations for researchers in a wide spectrum of disciplines.