## Darryl Revenew

JMP

Data Analysis with JMP® Statistical Discovery Software Campus Workshop

JMP includes comprehensive capabilities for statistical and graphical analysis of data for every academic field and most research needs. Easily perform basic statistics to the most complex analyses a PhD student may encounter. JMP runs on Windows and Macintosh operating systems and also functions as an easy, point-and-click interface to SAS®, R, MATLAB and Excel.

Thursday, August 21, 2014 - 3:30pm
Type:

## Nan Zhang

PhD Candidate, The University of Georgia Department of Statistics

Hilbert-Schmidt Independence Criterion in Sufficient Dimension Reduction and Feature Screening

This dissertation undertakes the theory and methods of sufficient dimension reduction in the content of Hilbert-Schmidt Independence Criterion (HSIC). The proposed estimation methods enjoy model free property and require no link function to be smoothed or estimated. Two tests: Permutation test and Bootstrap test, are investigated to examine the true underlying dimension of data considered. Sampling distribution of our estimator is established in single-index regressions. Root-n consistency of our estimator is proved for multiple-index models.

Major Professor(s):
Dr. Xiangrong Yin
Monday, April 21, 2014 - 1:00pm
Type:
The Cohen Room 230, Statistics Building

## Haileab Hilafu

PhD Candidate, The University of Georgia Department of Statistics

On Dimension Reduction and Feature Selection in High Dimensions

Sufficient Dimension Reduction (SDR) is a dimension reduction paradigm for reducing the dimension of the predictor vector without losing regression information. Classical inverse regression based SDR methods, though successfully used in many applications and have attractive computational properties, require inverting the predictor vector covariance matrix.

Major Professor(s):
Dr. Xiangrong Yin
Friday, April 18, 2014 - 3:30pm
Type:
Cohen Room 230, Statistics Building

## Yi Chen

PhD Candidate, University of Georgia Department of Statistics

Symbolic Data Regression and Clustering Methods

With the development of computing and internet technology, data sets with stupendously large numbers of observations are more and more common. One technique to handle the big data is to aggregate classical data to symbolic data, like lists, intervals, lists with probabilities and intervals with probabilities (histograms). Building clustering methods for symbolic data has been an active area over the past decade. In this dissertation, we first review regression and clustering methods for interval data.

Major Professor(s):
Dr. Lynne Billard
Friday, April 18, 2014 - 1:00pm
Type:
Cohen Room 230, Statistics Building

PhD Candidate, University of Georgia Department of Statistics

Matrix Time Series Analysis

Many data sets in the sciences (broadly defined) deal with multiple sets of multivariate time series. The case of a single univariate time series is very well developed in the literature; and single multivariate series though less well studied have also been developed (under the rubric of vector time series). A class of matrix time series models is introduced for dealing with the situation where there are multiple sets of multivariate time series data.

Major Professor(s):
Dr. Lynne Billard
Type:
Thursday, April 17, 2014 - 10:00am
Cohen Room 230, Statistics Building

## Rui Song

North Carolina State University

Sequential Advantage Selection for Optimal Treatment Selection

Dynamic treatment regimen is emerging as a new strategy for treatment which takes individual heterogeneity in disease severities, background characteristics and related clinical measurements into consideration. In this work, we propose a strategy to select variables with qualitative interactions to get the optimal treatment regime based on sequential advantage. We will demonstrate the proposed method with extensive numerical results and a real data analysis.

Thursday, January 9, 2014 - 3:30pm
Type:

## Jin Tang

PhD Candidate, University of Georgia Department of Statistics

Generalized Quasi-Likelihood Ration Test for Semiparametric Analysis of Covariance Models in Longitudinal Data

Semiparametric regression models have been wildly applied into the longitudinal data. In this dissertation, we model generalized longitudinal data from multiple treatment groups by a class of semiparametric analysis of covariance models, which take into account the parametric effects of time dependent covariates and the nonparametric time effects. In these models, the treatment effects are represented by nonparametric functions of time and we propose a generalized quasi-likelihood ration (GQLR) test procedure to test if these functions are the same.

Major Professor(s):
Yehua Li
Thursday, November 14, 2013 - 10:00am
Type:
Cohen Room 230, Statistics Building

## Yijuan Hu

Emory University

Integrative Analysis of Sequencing and GWAS Data for Rare Variant Associations

In the large cohorts typically used for genome-wide association studies (GWAS), it is not economically feasible to sequence all cohort members. A cost-effective strategy is to sequence subjects with extreme values of quantitative traits or those with specific diseases. By imputing the sequencing data from the GWAS data for the cohort members who are not selected for sequencing, one can dramatically increase the number of subjects with information on rare variants.

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

## Zhou Yu

School of Finance and Statistics, East China Normal University

Trace Pursuit: A General Framework for Model Free Variable Screening and Selection

We propose trace pursuit for model-free variable selection under the sufficient dimension reduction paradigm. Two distinct algorithms are proposed: stepwise trace pursuit and forward trace pursuit, both of which can be combined with many existing sufficient dimension reduction methods. Stepwise trace pursuit achieves selection consistency with fixed dimension p, and is readily applicable in the challenging p>n setting. Forward trace pursuit can serve as an initial screening step to speed up the computation in the case of ultrahigh dimensionality.

Thursday, February 20, 2014 - 3:30pm
Type:

## 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: