CLICK HERE TO JOIN
There has never been a better time to be a statistician.
Texas A&M Statistics is hoping to capitalize on this current trend and our decades of success as a department by embarking on a unique effort to benefit our former, current, and future students. But to do so, we’ll need your help.
We’ve started the Statistics Former Student Network to leverage the rich and varied experiences, expertise, and skill sets of our former students to help enrich and extend the educational experiences for our present and prospective graduate and undergraduate students.
Because we believe the short- and long-term success of such a powerful group begins and ends with its potential members, we value your input, ideas, and advice. If you have interest in serving on the board, have suggestions for talks or webinars, wish to assist with mentoring students, or have any other ideas for SFSN, please contact us!
One of Texas A&M University’s best assets is its people — successful former students, colleagues, and friends with a wealth of institutional, industry, and inspirational knowledge to offer. We encourage you to consider giving back by paying it forward. Advantage, Aggies!
CLICK HERE TO GET STARTED
A network of possibility, created by Texas A&M statisticians for Texas A&M statisticians!
Know someone who’s not receiving our emails? It might be because they haven’t updated their Aggie Network profile! To update your contact information, visit aggienetwork.com.
MOST RECENT WEBINAR
Scott Fincher, Data Scientist, KNIME, Inc.
“Open Source Modeling Integrations with KNIME”
October 24, 2018, 11:30 am (CST), Blocker 457
We will post a link to the recorded presentation within 48 hours of the event.
Missed the webinar? We’ve got you covered! |
CLICK HERE to play the recorded video.Our apologies for the poor sound quality. |
PAST WEBINARS
June 15, 2018
Shahina Rahman, Post-Doctoral Researcher, Texas A&M University
Fast Clustering of High Dimensional Data (Click to view)
February 28, 2018 (Inaugural Webinar)
Mikyoung Jun, Associate Professor of Statistics, Texas A&M University
Predictive Statistical Representation of Observed and Simulated Rainfall Using Generalized Linear Models (Click to view)
CONNECT WITH YOUR PEERS
SFSN Twitter, Facebook, and LinkedIn groups are coming soon! Also, stay tuned for links to an online forum for SFSN members, currently in development.
CURRENT BOARD MEMBERS
Please feel free to contact the board if you have questions about SFSN, or if we can assist you in any way.
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Dr. Ersen Arseven
PhD Statistics ’74 Statistical and Process Consultant Nyack, NY |
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Marcy Johnson
MS Statistics ’88 Director, Quantitative Research, MD Anderson Cancer Center Houston, TX |
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Dr. Shane Reese
Ph.D Statistics ’99 Dean, College of Physical and Mathematical Sciences, Brigham Young University Provo, UT |
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Scott Fincher
MS Statistics ’17 Data Scientist, KNIME Austin, TX |
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Dr. Jinhua Huang
PhD Statistics (UC Berkeley) 1997 Head, Statistics Department, Texas A&M University College Station, TX |
StatLinks
Calendar
February 2019
Faculty Sponsored Seminar: Shanshan Zhao
February 18, 201911:30 AM / 12:30 PM Blocker Building (BLOC), Room 457 979-845-3141
SHANSHAN ZHAO 
Biostatistics and Computational Biology Branch
National Institute of Environmental Health Sciences
Statistical Methods of Multivariate Failure Time Data
ABSTRACT
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
Departmental Colloquia: Kshitij Khare
February 22, 201911:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
KSHITIJ KHARE 
Department of Statistics
University of Florida
A Bayesian Approach for Joint Estimation of Multiple Networks
ABSTRACT
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
Departmental Colloquia: Veronika Rockova
March 1, 201911:30 AM / 12:30 PM Blocker Building (BLOC,) Room 113 979-845-3141