Upcoming Conferences, Fall 2018
Distinguished Professor, Bani Mallick (Organizer) and the Center for Statistical Bioinformatics proudly presents the Statistical Bioinformatics and Cancer Symposium. Registration is open! For more information on the conference, please click on the link above.
Associate Prof. Mikyoung Jun, Assistant Prof. Matthias Katzfuss and Associate Prof. Huiyan Sang (Organizers). Join us on Friday, September 28, 2018 and Saturday, September 29, 2018 at the Stephen Hawking Auditorium and Penrose Plaza in the Mitchell Institute at Texas A&M University. Registration is now open! For more information on this workshop, please click on the link above.
I Am Texas A&M Science - Alan Dabney
Even for the most seasoned academic researcher or staff member, career inspiration starts somewhere, and it all begins with a story. Here's one from Texas A&M statistician Alan Dabney, who discusses his field’s applications to public health in our most recent edition of I Am Texas A&M Science.
Texas A&M statistician Huiyan Sang has been selected to receive the American Statistical Association's 2018 ENVR Early Investigator Award, presented by the ASA Section on Statistics and the Environment (ENVR) in honor of her outstanding contributions to spatial and environmental statistics.Read More →
Jianhua Huang, a respected researcher and educator in statistical machine learning, computational statistics and statistical methods for big data sets, has been appointed as acting head of the Texas A&M Department of Statistics.Read More →
09:00 AM / 05:00 PM Blocker Building, Room 457, 453 979-845-3141
08:00 AM / 04:00 PM Hilton College Station Hotel & Conference Center 979-845-3141
11:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
Department of Statistics
Florida State University
Recent Advances in Elastic Functional Data Analysis
Functional data analysis (FDA) is fast becoming an important research area, due to its broad applications in many branches of science, including biostatistics and bioinformatics. An essential component of FDA is registration of points across functional objects. Without proper registration, the results are often inferior and difficult to interpret.
The current practice in FDA community is to treat registration as a pre-processing step, using off-the-shelf alignment procedures, and follow it up with statistical analysis of the resulting data. In contrast, Elastic FDA is a more comprehensive approach, where one solves for the registration and statistical inferences in a simultaneous fashion. The key idea here is to use Riemannian metrics with appropriate invariance properties, to form objective functions for alignment and to develop statistical models involving functional data. While these elastic metrics are complicated in general, we have developed a family of square-root transformations that map these metrics into simpler Euclidean representations, thus enabling more standard statistical procedures. Specifically, we have developed techniques for elastic functional PCA and elastic regression models involving functional variables. I will demonstrate this ideas using imaging data in neuroscience and bioinformatics, where biological structures can often be represented as functions (curves or surfaces) on intervals or spheres. Examples of curves include DTI fiber tracts and chromosomes while examples of surfaces include subcortical structures (hippocampus, thalamus, putamen, etc). Statistical goals here include shape analysis and modeling of these structures and to use their shapes in medical diagnosis.
Friday, 8/31/2018, BLOC 113, 11:30 AM