News and Events

The Statistics department has awarded over $50,000 in scholarships to 15 students for the 2021-2022 academic year. The awardees were selected from 33 applications, on the basis of academic achievement and research potential as well as a personal statement of professional goals. Recipients include undergraduates, master’s and PhD students, at all levels of their academic careers.

The recipients are

• Dunfu Yang – Fryer scholarship
• Christina Funk – Iman scholarship
• Ashley Pritchard – Iman scholarship
• Nathan Byard - Iman scholarship
• Kun Fan - Lin scholarship
• Jamberi Mangarama - Lin scholarship
• Yang Yang - Siepman scholarship
• Wu Hui - Statistics scholarship
• Allan Vieira de Castro Quadros - Waller scholarship
• Guotao Chu – Coyne scholarship
• Min Hua – Coyne scholarship
• Yiming Li – Coyne scholarship
• Linus Addae – Coyne scholarship
• Narmadha Mohankumar – Coyne scholarship
• Linruo Guo - Coyne scholarship

Congratulations to all!

More information about these scholarships: Finance for Undergraduates or Finance for Graduate Students.

Type 2 diabetes mellitus (T2DM) affects the lives of millions of people through its life-altering complications. Current methods of identifying genetic polymorphisms responsible for T2DM face the limitation of sample size and low accuracy at the population level (AUC of 0.68 or below). This research presents a method to identify subtle effects of genetic variants using whole genome sequencing data and improve prediction accuracy of T2DM at the population level. To achieve this, a new feature selection procedure and a classier were proposed. The method involves (1) first applying sparse principal component analysis (PCA) to genotype data to obtain orthogonal features; (2) using SNP-specific regularization parameters to reduce the false positive rate of feature selection; (3) verifying feature relevance through Lasso penalized logistic regression in conjunction with sparse PCA. After applying to a dataset containing 625,597 SNPs and 23 environmental variables from each of 3,326 humans, the method identified over 450 genetic variants that each have subtle effects on T2DM prediction. These variants, in conjunction with clinical characteristics, led to greatly improved prediction accuracy (AUC 0.79) for new patients at the population level. The proposed method also has the advantage of computational efficiency, which is 20 times faster than Random Forest classifier, and thus provides a promising tool for large-scale genome-wide association studies.

Joint work with Luann C Jung at Massachusetts Institute of Technology, Xukun Li and Cen Wu at Kansas State University.

The Statistics department awarded over $40,000 in scholarships to nine students for the 2018-2019 academic year. The awardees were selected from 38 applications, on the basis of academic achievement and research potential as well as a personal statement of professional goals. Recipients include undergraduates, master’s and PhD students, at all levels of their academic careers.

More information about the scholarship recipients

Beginning in Fall 2018, the Statistics department will offer two new options for obtaining a degree in Statistics. For graduate students, the MS degree has a new track in Data Science and Analytics that emphasizes practical computational and modeling skills necessary to accommodate "big data" systems. For undergraduates, the accelerated BS/MS option allows students to earn both a BS and an MS in less time than it would take to earn each degree separately.

For more information

• Data Science and Analytics
• Accelerated BS/MS

The Statistics department will offer four workshops during summer 2018 that focus on recent developments in statistical methodology and computational techniques.

• Introduction to Spatial Analysis in R
  Dr. Trevor Hefley - May 18
• Statistical Machine Learning for High Dimensional Data
  Dr. Cen Wu - June 1
• Applied Classical and Modern Multivariate Statistical Analysis
  Dr. Juan Du and Dr. Weixing Song - August 15
• Mixed Models for Agricultural and Biological Research
  Dr. Nora Bello - date TBD

These workshops are sponsored in part by Shell Oil. They are offered at no cost to KSU affiliates, but space is limited. Registrants will be accepted on a first-come first-served basis.
Workshop Details

The University of Kansas Center for Research Methods and Data Analysis (CRMDA) will host its annual three-week Statistical Institute, also referred to as Stats Camp, next month. The three-week Stats Camp will run from May 21 to June 8, 2018 on the Lawrence campus. Each week has a designated topic: R software, Python data analysis and structural equation modeling. Full topic listings and registration details are posted on the CRMDA website (crmda.ku.edu/statscamp).

The sessions are a combination of lecture format presentations and workshops for "hands on" practice. CRMDA recommends everyone who attends should bring a laptop computer on which they have administrative privileges so that new software can be installed. There will be plenty of workshop "helpers" to troubleshoot problems during the workshop sessions. CRMDA has experience with Linux, MS Windows, and Macintosh computers (and configuration advice on http://crmda.ku.edu/setup). Attendees can expect to learn statistical terminology and computing tools, obtain strategies for reproducible research and enhance their research techniques by developing new skills.

CRMDA will also offer remote access attendance via Zoom for people who cannot attend in person.

• Week 1 ( May 21-25) - Using R
  Covers the basics of interacting with R, importing data, creating graphics, and conducting statistical analysis. This will introduce tools for project management that are offered in our R package, "kutils".
• Week 2 ( May 29-June 1) - Python Data Science
  As in the R workshop, covers basics of interacting with Python, including how to navigate around Jupyter Notebooks, working with text data, using Pandas and scraping Internet data.
• Week 3 ( June 4-8) - Structural Equation Modeling

For more information, download the flyer.

Congratulations to Haoyu Zhang, who has received an Arts & Sciences Research Travel Award. She will attend the 2018 Joint Statistical Meetings (JSM) at Vancouver, Canada, to present her research “Modeling abundance of multiple species using latent regression tree algorithms”. Ms. Zhang’s major professor is Dr. Trevor Hefley, and this research is in collaboration with Brian R. Gray and Kristin Bouska, both of the USGS. She expects to graduate in Fall 2019.

Abstract

Nelson Walker has been awarded an honorable mention for National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP). Nelson is one of six current Kansas State University students and two alumni who received an award. They were selected from more than 12,000 applicants from all 50 U.S. states, including D.C. and the U.S. territories.

Read more ...

Dr. Nora Bello led a workshop Mixed Models in Agriculture, May 31 - June 2, 2017. This short course provided a comprehensive exposition of mixed-model based statistical data analysis, power determination and sample size calculation for commonly used experimental designs in the agricultural and biological sciences. The workshop was made possible by a grant from Shell Oil, facilitated by a former PhD student Seth Demel (2013), who worked under Dr. Juan Du.

Introduction to SAS Computing (STAT 725) and Introduction to R Computing (STAT 726) are offered online beginning Spring 2018. These courses were developed by Karen Keating, with funds from Global Campus.

Audrey Chang has received an OURCI Research Award from the Office of Undergraduate Research & Creative Inquiry for Fall 2017. Audrey is an undergraduate statistics major, working with Dr. Wei-Wen Hsu. She will use this award to continue her research into using a zero-inflated Poisson model coupled with working independence assumptions to analyze the longitudinal tornado data in the state of Kansas from 1950 to 2015.

The Statistics department has awarded nearly $45,000 in scholarships for the 2017-2018 academic year.

Iman scholarships were given to incoming freshman Brock Kaufmann and undergraduate statistics major Vincent Sylvester. Graduate students Xintong Li and Kessinee Chitakasempornkul each received a Lin scholarship. Jie Ren and Chenshuang Lu, both graduate students, received the Fryer and Siepman scholarships, respectively. Graduate student Fei Zhou was awarded the Statistics scholarship, and undergraduate student Audrey Chang received the Waller scholarship. Coyne scholarships were given to graduate students Yinhao Du, Nelson Walker, Haoyu Zhang and Huaiyu Zhang.

These scholarships are made possible by generous support from prior faculty and graduates of the Department of Statistics.

• Lolafaye Coyne Statistics Graduate Scholarship. Lolafaye Coyne received her Ph.D. from our department in 1972. She established this scholarship to support graduate students properly enrolled in the Department of Statistics in the College of Arts and Sciences at Kansas State University. First preference is given to students planning a career in Statistics.
• The Holly and Beth Fryer Scholarship. Holly Fryer was the first statistician hired into the University in 1940, and was instrumental in establishing the Department of Statistics in 1959. He was the first department head, and served in that position until 1974. He was named ASA Fellow in 1966. The Fryer scholarship is available to any regularly enrolled student at KSU who is majoring in statistics. Scholastic performance is the criterion used in selecting a recipient.
• The Ronald and Rae Iman Scholarship. Ron Iman received his Ph.D. from the department in 1973, and has been an ardent supporter of the department and K-State throughout his career. He was named ASA fellow in 1982 and received the ASA Founders Award in 1991. He currently serves on KSU Foundation Board of Trustees. The Iman scholarship is open to students who are properly enrolled in the Department of Statistics with preference to undergraduate students from rural Kansas high schools.
• Dr. Lynn Ying-Shiang Lin Statistics Graduate Research Scholarship. Lynn Lin is a 1963 graduate of K-State with a master’s degree in statistics, and went on to earn a doctoral degree in statistics from the University of Minnesota. In 2009, he was named alumni fellow by K-State’s College of Arts and Sciences. The recipient of the Lin scholarship is a graduate research assistant in good academic standing. Preference is given to students with financial need who are involved in food safety or clean water research.
• Ray and Carolyn Waller Statistics Scholarship. Ray Waller has been both a student and faculty member in the Department of Statistics. He was named ASA Fellow in 1996. The Waller scholarship is available to both undergraduate and graduate students properly enrolled in the Department of Statistics in the College of Arts and Sciences at Kansas State University.
• Howard Siepman Memorial Scholarship. Howard Siepman received his Ph.D. from the Department of Statistics in 1987. This scholarship is available to any PhD graduate student properly enrolled in the Department of Statistics at Kansas State University.

More information about financial support in the Department of Statistics is available at the following links:

• Financial Support for Undergraduate Statistics students
• Financial Support for Graduate Statistics Students

Jie Ren will use this travel award to present her research at JSM 2017.

Title: Robust Network-based Regularization and Variable Selection for High-Dimensional Genomic Data in Cancer Prognosis

This work was initialized as a class research project when Jie attended Dr. Cen Wu’s High-Dimensional Data and Statistical Learning class in Fall 2016. Yinhao Du (PhD student) and Dewey Molenda (undergraduate student) also participated in the project. Jie is the first statistics graduate student who has received graduate student travel award from Johnson Cancer Research Center.

More information about Jie's presentation is available in the JSM online program.

Dr. Cen Wu has received an Innovative Research Award from the Johnson Cancer Research Center. He plans to study high dimensional statistical methods for lipid-environment interactions.

Title: Integrative Lipid - Environment Analysis for Cancer Prevention Studies

Abstract: Lipid species are critical components of eukaryotic membranes and play key roles in many biological processes. Investigations of lipid-environment interactions, in addition to the lipid and environment main effects, have important implications in understanding the lipid metabolism and related cancer prevention. The objective of this project is to develop novel regularized variable selection methods to improve the accuracy and reliability in identification of important lipid-environment interaction effects in cancer prevention studies. Furthermore, powerful computational tools and software packages will be developed to analyze the high dimensional lipidomics data. This research project has been partially motivated by Dr. George Wang (the co-PI)’s cancer prevention study on lipid profiling of weight controlled mice in both skin and plasma samples.

Behnaz Moradi has been awarded a scholarship and travel award for the Summer Institute in Statistical Genetics (SISG), held at the University of Washington. The institute provides an introduction to modern methods of statistical analysis and challenges posted by modern genetic data. Many of these topics are relevant to Behnaz's current research on network analysis. The institute and scholarships are supported by the NIH National Institute of General Medical Sciences (NIGMS). Behnaz is working with Dr. Michael Higgins.

Dr. Cen Wu and Dr. Mike Higgins have each received a travel award to attend the Sackler Colloquium on ‘Reproducibility of Research: Issues and Proposed Remedies’ at the National Academy of Sciences, March 8-10 2017. These funds were made available through a generous donation from the John Templeton Foundation, the University of Alabama at Birmingham. More information about the meeting can be found here: http://tinyurl.com/hc7nb7m.

The Statistics Department has received a $5000 grant from Shell Oil. This grant was facilitated by a former PhD student Seth Demel (2013), who worked under Dr. Juan Du. The grant will be used to support a scholarship for a Statistics graduate student whose research has applications in industry, and will also be used to deliver two faculty-led workshops on topics in applied statistics.

The first workshop will be for spatial statistics. It is scheduled for May 19 and is coordinated by Dr. Trevor Hefley. More information about the spatial statistics workshop.

The second workshop will be conducted by Dr. Nora Bello May 31 - June 2 and will cover mixed models for agriculture and biological research. More information about the mixed model workshop.

Audrey Chang and Graham Seacat have each been awarded an Arts & Sciences Undergraduate Research Scholarship for 2016-1017. Audrey is working with Dr. Wei-wen Hsu and Graham is working with Dr. Trevor Hefley.

Audrey's Abstract

The purpose of this project is to use a zero-inflated Poisson model coupled with working independence assumptions to analyze the longitudinal tornado data in the state of Kansas from 1950 to 2015. The zero-inflated models are used often to accommodate excess zero counts in data. In the Kansas tornado data, the number of counties with no tornado touch downs during a certain period of time was often observed, therefore creating many zero counts. We started this project in August 2016, with Dr. Wei-Wen Hsu of the Statistics Department as my mentor. Through the proposed model, we will identify factors (e.g., location of county, season, temperature, etc.) that can be used to predict the frequency of tornado touch-downs in the future. We expect that the model can provide a new perspective about tornado monitoring and gain new understanding to Kansas’ tornado patterns.

This first semester, we have set aside two months reading and understanding of the data and related literature about the tornadoes and zero-inflated Poisson models, respectively. We are currently finishing up the data preparation (i.e., data merging and data cleaning) using statistical software SAS and will focus on the model development and data visualization for Kansas tornado data in the following few months. Starting February 2017, we will write a scientific paper and hope to publish our findings in a scientific journal afterwards as well as present our findings at a national or international conference.

Graham's Abstract

The purpose of this study is to implement statistical analysis in the field of ecology to learn more about the behavior of mule deer (Odocoileus hemionus). Animal behavior is the link between the biology of the animal and the ecosystem in which it lives. I plan to apply our movement modeling approach to a telemetry dataset, collected from GPS devices that measure location and heart rate, on mule deer from the Piceance basin in Colorado, USA. The data points typically are separated into two “behavioral states”, moving and sleeping. Unlike many telemetry data sets, the proposed dataset is ideal to test our methods because field based observations that provide visual proof of the animals’ behavior are also available.

For more information about undergraduate research opportunities in the College of Arts & Sciences, visit http://artsci.k-state.edu/research/undergraduate/

Kessinee Chitakasempornkul will present her research at the annual Conference of Research Workers in Animal Disease (CRWAD) conference in December 2016. Title: Accounting for data architecture on structural-equation-based modeling of feedlot performance outcomes.

Jiena Gu has joined the Beef Cattle Institute as a Project Coordinator and is highlighted in the article "Storytelling with Big Data", which appeared in the August 2016 issue of The Grazier, a publication of the the Beef Cattle Institute.

Read the full article.

Jiena Gu received a Master's degree in Statistics from KSU in 2016. Her major advisor was Dr. Wei-Wen Hsu and her master's report was entitled "Monitoring the Progression of Alzheimer's Disease with Latent Transition Models".

The Graduate School has selected Sharif Mahmood as one of sixteen graduate students to serve as Graduate Student Ambassadors for the 2016-2017 academic year.

Ambassadors are a diverse and supportive group of students selected to provide campus tours and help answer questions from prospective students on what makes the K-State community so great. They are active on Graduate School's social media, sharing their experiences as graduate students.

Ambassadors come from a variety of academic backgrounds and disciplines, from large cities and small towns all around the world, each with a unique perspective.

Learn more about the Graduate Student Ambassadors online.

Audrey Chang has received an award for Undergraduate Research in the College of Arts & Sciences for Fall 2016. She will be working with Dr. Wei-Wen Hsu to develop a zero-inflated Poisson model for Kansas' tornadoes.

Abstract

The purpose of this project is to use a zero-inflated Poisson model coupled with working independence assumptions to analyze the longitudinal tornado data in the state of Kansas from 1950 to 2014. The zero-inflated models are used often to accommodate excess zero counts in data. In the Kansas tornado data, the number of counties with no tornado touch downs during a certain period of time was often observed, therefore creating many zero counts. Through the proposed model, we will identify factors (e.g., location of county, season, etc.) that can be used to predict the frequency of tornado touch downs in the future. We expect that the model can provide a new perspective about tornado monitoring and gain new understanding to Kansas’ tornado patterns.

Research Plan

The first two months will be set aside for reading and understanding of the data and related literature about the tornadoes and zero-inflated Poisson, respectively. The next three months we will conduct real data analysis using R and/or SAS as well as some data managements (i.e., data merging and data cleaning). The rest of the year will be used to write a scientific paper of the findings. We hope to publish our findings in a scientific paper afterwards.

For more information about undergraduate research opportunities in the College of Arts & Sciences, visit http://artsci.k-state.edu/research/undergraduate/

The Statistics Department awarded three graduate degrees in Spring 2016.

• Bo Tong completed his Ph.D., working with Dr. Haiyan Wang. The title of his dissertation is "More Accurate Two Sample Comparisons for Skewed Populations".
• Xiaojing Zhang completed her Master's degree, working with Dr. James Neill. Her master's report is "A Simulation Study of Confidence Intervals for the Transition Matrix of a Reversible Markov Chain".
• Chendi Cao completed his Master's degree, working with Dr. Weixing Song. His master's report is entitled "Linear Regression with Laplace Measurement Error".

In addition, Alex McClellan and Jiena Gu will complete their degrees this summer.

Congratulations, graduates!

Photo gallery

Dissertations and Master's reports can be viewed in their entirety at K-State Research Exchange (K-REX).

Nine departmental scholarships have been awarded for 2016-2017.

• Audrey Chang - Ronald and Rae Iman Scholarship
• Angel Zelazny - Ronald and Rae Iman Scholarship
• Huaiyu Zhang - Holly and Beth Fryer Scholarship
• Nadeesha Mawella - Holly and Beth Fryer Scholarship
• Xintong Li - Howard Siepman Memorial Scholarship
• Guotao Chu - Statistics Department Scholarship
• Sharif Mahmood - Ray and Carolyn Waller Scholarship
• Mengjiao Wu - Ray and Carolyn Waller Scholarship
• Xukun Li - Arthur D. and Lavonia B. Dayton Scholarship

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. View background on his research.

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 (pdf)
Other 2016 KSU Alumni Fellows

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.

Summary of A&S Faculty Enhancement Program

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".

Other Fall 2015 Awards from the Office of Vice President for 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.