News and Events
R. Dennis Cook has been named 2016 KSU Alumni Fellow for the College of Arts & Sciences. Dr. Cook is currently a full professor and director of the School of Statistics at the University of Minnesota. He earned a master's degree in statistics from K-State in 1969 and a doctorate, also in statistics from K-State, in 1970. At the University of Minnesota, he has served a ten-year term as Chair of the Department of Applied Statistics, and a three-year term as Director of the Statistical Center.
His research areas include dimension reduction, linear and nonlinear regression, experimental design, statistical diagnostics, statistical graphics and population genetics. He has authored over 200 research articles and is author or co-author of two text books – An Introduction to Regression Graphics, and Applied Regression Including Computing and Graphics – and two research monographs, Influence and Residuals in Regression, and Regression Graphics: Ideas for Studying Regressions through Graphics. Background on these works can be found at http://www.stat.umn.edu/~dennis/.
In a seminal 1977 publication, Dr. Cook introduced Cook’s Distance (Cook’s D), a widely used statistic that measures the relative influence of each individual case in a sample of data on the results of a regression analysis. It is used to discover whether one or more cases have such a large effect that they might distort the overall results of the regression.
Dr. Cook has served as Associate Editor of the Journal of the American Statistical Association, The Journal of Quality Technology, Biometrika, Journal of the Royal Statistical Society and Statistica Sinica. He is a four-time recipient of the Jack Youden Prize for Best Expository Paper in Technometrics as well as the Frank Wilcoxon Award for Best Technical Paper. He received the 2005 COPSS Fisher Lecture and Award, the highest honor conferred by the statistics profession. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute.
The KSU Alumni Fellowship program was established in 1983 to recognize distinguished alumni. Each year, faculty members from each of K-State’s colleges nominate alumni that they believe deserve the title of Alumni Fellow. The dean of each college then makes the final decision and the person selected is invited to be a part of a three-day celebration in their honor. Events usually include a cocktail party at the president’s home, classroom visits, speaking during a luncheon or at an educational panel, and a dinner with all fellows and their families. This year, Alumni Fellows will be on campus April 6-8.Curriculum Vitae: http://users.stat.umn.edu/~rdcook/CookPage/cookcv.pdf
Other 2016 KSU Alumni Fellows: http://www.k-state.com/s/1173/social.aspx?sid=1173&gid=1&pgid=561
Gyuhyeong Goh and Mike Higgins have each won an award in the Arts & Sciences Faculty Enhancement Program. This program was established in 2011 to promote independent research among new faculty members, consistent with K-State's Vision 2025 goals.
Gyuhyeong Goh plans to study Bayesian Functional Regression Modeling of Transcriptional Regulatory Networks. Understanding gene regulatory networks is a highly significant problem to interpret the phenotypic consequences (e.g. disease) of genetic variation. The study of transcriptional regulatory mechanism, however, involves several difficulties. First, it is hard to directly measure the actual activity of transcription factors due to the lack of technology. Second, a small number of transcription factors, that are significantly related to a given biological process, should be identified from a large pool of candidates, often referred to as sparse high-dimensional problems. Third, since a biological process is dynamic, it requires a time-course investigation into the temporal behavior of transcription factors during the biological process rather than at a single time point. In this project, our major goal is to develop a functional regression method to unveil the hidden transcriptional regulatory networks from a Bayesian perspective. The Bayesian approach enables us to incorporate prior knowledge about relevant transcription factors into a posterior inference procedure. The proposed project will broaden and deepen our understanding about transcriptional regulatory networks by providing us a general tool to investigate the consequences of genetic variation.
Mike Higgins plans to study Extensions of Threshold Blocking to Problems in Big Data and Causal Inference. Threshold blocking for an experiment is the process of grouping similar units together before assigning treatment so that each group contains at least a pre-specified number of units. Previous work involved developing a highly efficient algorithm for generating an approximately optimal threshold blocking in massive experiments. For example, using our method, experiments with 10 million units can be blocked in seconds using a personal computer. This project involves extending this procedure to other problems in Causal Inference and Big Data. We apply our method to the problem of finding regions of covariate overlap between treatment groups in observational studies. We also investigate the use of our algorithm as a data preprocessing technique to improve performance of prediction and clustering algorithms in massive datasets.
Juan Du and Weixing Song have each received a Faculty Development Award from the Office of Vice President for Research for Fall 2015. Juan Du will travel to the 3rd Conference of the International Society for NonParametric Statistics, Avignon, France, to present "Spatial functional modeling of weather change impact on corn yield in Kansas". Weixing Song will travel to the International Conference on the Frontier of Statistics in Beijing, China, to present "Statistical inferences with Laplace measurement errors".
A hearty congratulations to the following scholarship recipients. More information about these scholarships can be found on our assistantship page.Audrey Chang - Ronald and Rae Iman Scholarship
Seth Raithel publishes M.S. research
A manuscript based on the research conducted by Seth Raithel (M.S., 2015) has been accepted for publication by BMC Genomics. Seth's adviser was Dr. Nora Bello, and the title of the manuscript is "Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii". A full copy of his MS report can be found on K-REx.