Statistics Undergraduate Students Turned on the Charm at Aggieland Saturday 2019!
An annual campus-wide open house for prospective students and their families. These bright students dazzled potential Aggies while showcasing our Undergraduate Program. Prospects were able to meet current students, attend departmental presentations, tour the dorms, visit libraries and computer labs and see everything that Texas A&M has to offer. Students could also learn more about colleges and majors, get information about admission and pick up some financial aid and scholarship information, too.
Showing their school spirit is what they do best! Pictured here are current undergraduate students. GIG 'EM!
I Am Texas A&M Science - Irina Gaynanova
Even for the most seasoned academic, career inspiration starts somewhere, and it all begins with a story. Here's one in our I Am Texas A&M Science series from Texas A&M statistician Irina Gaynanova, who, despite not exactly loving her academic field as an undergraduate, grew to appreciate working with data enough to make a career out of developing statistical methodology for high dimensional data problems.
Jianhua Huang, a respected researcher and educator in statistical machine learning, computational statistics and statistical methods for big data sets, has been appointed as associate director of the Texas A&M Institute of Data Science.Read More →
Texas A&M statistician Derya Akleman will work closely with each of the college's five departments and across the university in her new administrative role as assistant dean for diversity and college climate in the College of Science.Read More →
11:30 AM / 12:30 PM Blocker Building (BLOC), Room 457 979-845-3141
Biostatistics and Computational Biology Branch
National Institute of Environmental Health Sciences
Statistical Methods of Multivariate Failure Time Data
Many biomedical studies follow participants for multiple correlated health outcomes. Modeling these outcomes simultaneously opens the possibility of understanding an individual’s susceptibility to multiple diseases throughout the lifespan. While statistical methods for univariate failure time data are well established, the corresponding standard analysis tools for multivariate failure time data have not yet been established. The main difficulty is that with multiple censored time-to-event outcomes, the joint likelihood is not uniquely defined due to uninformative data points concerning the local dependency between event times. This talk will focus on some recent development in this area, including a nonparametric and a semiparametric approach of estimating the joint survival function. These proposed methods have the ability to explore and estimate dependency between event times as well as to understand the relationship between dependency and risk factors. Simulation evaluations as well as an application to the Women’s Health Initiative’s hormone therapy trial will be presented.
Monday, 2/18/2019, 11:30 AM, BLOC 457
11:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
Department of Statistics
University of Florida
A Bayesian Approach for Joint Estimation of Multiple Networks
In this paper, we develop a novel Bayesian approach for joint estimation of multiple graphical models. This problem arises in many applications, such as understanding co-expression networks from high-dimensional Omics data obtained from different biological conditions or disease subtypes. We pursue a pseudo-likelihood based approach which provides robustness and computational efficiency. We illustrate the efficacy of our approach using simulated and real datasets.
This is joint work with George Michailidis and Peyman Jalali.
Friday, 2/22/2019, 11:30 AM, BLOC 113
11:30 AM / 12:30 PM Blocker Building (BLOC,) Room 113 979-845-3141