FTC 2022 Short Courses

Short courses will be held on October 12, 2022. Attendees may registers for the conference and short courses together or separately. Conference attendance is not required to attend a short course.


8:30am to 5:30pm (1 hour lunch at 12:30pm)


Advanced Quality Control Methods: CUSUM and EWMA Procedures

This course has been cancelled

Methods for Designing & Analyzing Mixture Experiments

This course has been cancelled


Functional Data Analysis and Its Applications
8:30am to 12:30pm

Pang Du, Virginia Tech

This course aims to introduce the modern field of functional data analysis to a general audience with the emphasis on how the relevant techniques can be applied to real examples. As a a generalization of the traditional data concepts from numbers and vectors of numbers to curves and surfaces, functional data has attracted a lot of attention from statisticians and found many interesting applications in a variety of fields in the past decades. The course will start with the introduction of real examples for functional data. Based on these examples, common functional data analysis techniques such as function smoothing, functional principal component analysis, and functional linear regression models will be presented. R implementation of these techniques will be introduced and demonstrated.

CONTACT: pangdu@vt.edu

DR. PANG DU graduated from Purdue University with PhD in Statistics in 2006 and has since then been a faculty member at the Department of Statistics, Virginia Tech. His research interest lies in both the development of new statistical methodology and novel applications of statistics to various studies in other disciplines. His research domain on statistical methodology includes functional data analysis, nonparametric smooth modeling, high dimensional data, failure time data analysis, change point analysis, and ROC curve methodology. His application areas include public health, biomechanical engineering, biomedical engineering, cybersecurity, environmental sciences, and biological sciences. His research has been supported by 9 research grants, including PI on 3 NSF grants, and co-I on a recent NIH R01 grant. He has over 50 publications in prestigious journals such as Annals of Statistics, Biometrika, Journal of the American Statistical Association, Annals of Applied Statistics, Biometrics, Statistica Sinica, and Statistics in Medicine.

He has conducted research on functional data analysis for more than a decade. His experience has led him to develop a new graduate student topic course, STAT 5554 Functional Data Analysis, at Virginia Tech. The course has attracted a total of over 30 graduate students, including 3 from non-statistics disciplines, since its debut a few years ago. His FTC short course will be an application-driven version with emphasis on hands-on experience with common functional data analysis techniques.

Non-parametric Approaches to Uncertainty and Hypothesis Tests
1:30pm to 5:30pm

Robert Richardson, Brigham Young University

This course is aimed to highlight several ways that non-parametric methods can be used to derive answers from data where other parametric methods are not readily available. We will cover the basic concepts of bootstrapping and permutation tests and compare them against parametric methods for scenarios where both can be used. A variety of scenarios will be discussed where the non-parametric methods are more feasible. We will show how to use these methods to get confidence intervals and perform hypothesis tests for parameter estimates, predictions, or model-derived statistics. We will also show how to use these methods to perform more advanced hypothesis tests on data such as testing for independence between variables, correlation structures of dependent data, and randomness of missing values. We consider a variety of statistics (mean, trimmed mean, regression, etc.), and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We’ll look at applications from a variety of fields, including telecommunications, finance, and biopharma.

CONTACT: richardson@stat.byu.edu

DR. ROBERT RICHARDSON is an Associate Professor at Brigham Young University. He received his PhD in Statistics and Applied Mathematics from University of California – Santa Cruz and is an associate of the Society of Actuaries. He has and is currently serving on several professional and academic committees aimed towards data science education. He currently serves as the chair of the SOA committee for  Advanced Topics in Predictive Analytics.