Min Yang

The University of Illinois at Chicago

An Algorithm Approach of Deriving Bayesian Optimal Designs

The usefulness and popularity of nonlinear models have spurred a large literature on data analysis, but research on design selection has not kept pace. One complication in studying optimal designs for nonlinear models is that information matrices and optimal designs depend on unknown parameters. Besides the popular locally optimal designs strategy, another common approach is to use Bayesian optimal design approach, which typically means an optimality problem has to be solved through numerical approaches. However, very few algorithm approaches are available for Bayesian optimal design.

Thursday, February 27, 2014 - 3:30pm
Type: 

Jason Kao

Arizona State University

Recent Developments in Optimal Experimental Designs for Functional MRI

Functional magnetic resonance imaging (fMRI) is one of the leading brain mapping technologies for studying brain activity in response to mental stimuli. For neuroimaging studies utilizing this pioneering technology, there is a great demand of high-quality experimental designs that help to collect informative data to make precise and valid inference about brain functions. In this talk, I provide a survey on some recently developed analytical and computational results on fMRI design selection.

Thursday, January 30, 2014 - 3:30pm
Type: 

Joyee Ghosh

The University of Iowa

Bayesian Variable Selection in the Presence of Multicollinearity

In this talk we will give a quick overview of some of the strengths and challenges in Bayesian variable selection as it evolved over the last two decades. We will then discuss two specific problems in linear regression with strong multicollinearity among the covariates.

Thursday, January 16, 2014 - 3:30pm
Type: 

Hsin-Ping Wu

PhD Candidate, University of Georgia Department of Statistics

Locally Optimal Designs for Generalized Linear Models with a Single-Variable Quadratic Polynomial Predictor

Finding optimal designs for generalized linear models is a challenging problem. Recent research has identified the structure of optimal designs for generalized linear models with a single or multiple independent explanatory variables that appear as first-order terms in the predictor. We consider generalized linear models with a single-variable quadratic polynomial predictor under a popular family of optimality criteria.

Major Professor(s): 
Dr. John Stufken
Type: 
Wednesday, November 13, 2013 - 10:00am
Statistics Building, Cohen Room 230

Snehalata Huzurbazar

SAMSI, University of Wyoming and North Carolina State University

Statistical Issues in the Inference of Timing of Gene Duplications

Gene duplication is the key mechanism for evolutionary change. To infer the timing and nature of gene duplication, the 'data' used are the end result of various pipelines. In this talk, I will summarize how the 'data' are obtained, explore the shortcomings of analyses in the literature, and end with current work on overcoming these shortcomings. The interesting statistical problems are that the 'data' are maximum likelihood estimates, and that the biological process (saturation effects) present complications in data modeling.

Thursday, March 27, 2014 - 3:30pm
Type: 

C.F. Jeff Wu

Georgia Institute of Technology

From Real World Problems to Esoteric Research: Examples and Personal Experience
TBA

Dr. Wu will speak at this year's Bradley Lecture on Friday April 25, 2014 at 4:30pm.  An RSVP is required to attend this lecture.

Friday, April 25, 2014 - 3:30pm
Type: 

R. Todd Ogden

Columbia University

Functional Data as Predictors in Regression Models with Scalar Outcomes

One situation that arises in the field of functional data analysis is the use of imaging data or other very high dimensional data as predictors in regression models.  A motivating example involves using baseline images of a patient's brain to predict the patient's clinical outcome.  Interest lies both in making such patient-specific predictions and in understanding the relationship between the imaging data and the outcome.  Obtaining meaningful fits in such problems requires some type of dimension reduction but this must be done while taking into account the particular (spatial) structure o

Thursday, April 10, 2014 - 3:30pm
Type: 

Richard De Veaux

Williams College, Williamstown

Modeling the Effect of Age in Sports Performance

The Dipsea is a 100 year old 8 mile running event that starts in Mill Valley CA and ends at the Pacific Ocean near Stinson Beach. What makes the event unique is its handicap system. Each age group for men and women receive a handicap time. For example, the slowest group, the AAA group, comprised of men 74 years old and older, boys 6 and under, women 66 and older, and girls 7 and under, receive a 25 minute handicap. But what makes the event unique is that each group starts ahead of the scratch group by that amount. So first to leave, at 8:30 AM, is the AAA group.

Tuesday, February 18, 2014 - 3:30pm
Type: 

Kathryn Chaloner

University of Iowa

Bayesian Methods for Study Design and Statistical Analysis

Dr. Chaloner will speak at this year's UGA/Clemson Joint Seminar on Thursday April 17, 2014 at 3:45pm.  An RSVP is required to attend this lecture.


Bayesian methods for statistical analyses require a different interpretation of probability than traditional “frequentist” methods.  The use of Bayesian methods is increasingly common and its flexibility has facilitated a wide range of scientific advances, especially in medicine.

Thursday, April 17, 2014 - 3:30pm
Type: 
UGA Hotel and Conference Center

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