Recent technological advances have enabled astronomers and cosmologists to collect data of unprecedented quality and quantity. These large data sets can reveal more complex and subtle effects than ever before, but they also demand new statistical approaches. This project consists of two intertwined components: (a) development of new nonparametric statistical methods that address recurrent problems in the analysis of astrophysical and cosmological data and (b) application of the new methods to help answer significant astrophysical and cosmological questions. Specifically, this research will improve inference for the Cosmic Microwave Background spectrum by constructing uniform confidence sets in nonparametric regression, characterize the influence of local environment on galaxy evolution by developing new methods for nonparametric errors-in-variables problems, and estimate the matter density from magnitude limited galaxy surveys by producing accurate density estimators for doubly truncated data.
The research provides interdisciplinary training for postdoctoral fellows and graduate students, and strengthens an interdisciplinary infrastructure between the mathematical and physical sciences.
