"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

Regression Model with Tree Structured Response

Yuan Wang, M.S. Candidate, Department of Statistics, Colorado State University

Tuesday, April 6, 2010

2:00 p.m., room 008, Statistics Bldg


Highly developed science and technology from last two decades motivated the study of complex data objects, which often lie in non-Euclidean spaces. In this paper, we considered tree-structured object, in particular its associated topological property. Our interest centers on modeling the relationship between tree-structured response variable and numerical covariates. Regression analysis has been a very powerful and widely-used toolkit for such purpose. For tree objects, this poses serious challenges since most regression methods relies on the linear operations in Euclidean space. We generalize the notion of local polynomial regression to the case of tree-structure response variable. In addition, a fast algorithm with theoretical justification is available. Our proposed method has been implemented to a data set of human's brain blood vessel trees.

Advisory Committee:

Dr. Haonan Wang, Advisor
Dr. F. Jay Breidt, Committee Member
Dr. Jie Luo, Electrical & Computer Engineering, Outside Member