Emmanuel Tuglo

PhD Candidate, University of Georgia Department of Statistics

Analysis of Univariate and Multivariate Longitudinal Data with Censored and Missing Response with Complex Covariance Structure

The analysis of univariate and multivariate longitudinal data (U/MLD) with censored and missing response has inspired considerable interest in the statistical community recently. In the case of MLD, estimating the contemporaneous correlation coefficient is of particular interest to applied researchers.

Major Professor(s): 
Dr. Daniel Hall
Wednesday, July 2, 2014 - 10:00am
The Cohen Room (230), Statistics Building

Xiaoming Huo

National Science Foundation and Georgia Tech

Funding for Statistical Science at National Science Foundation, and a New Result on Dependence

This talk will have two components. In the first part, I will give an overview of NSF, and its resources that are relevant to the statistical science. Some related programs that are beyond the Division of Mathematical Science, such as the Big Data program and the Computational and Data-enabled Science and Engineering program, will be reviewed. Some suggestions regarding how to apply to these programs may be provided.

Tuesday, September 2, 2014 - 3:30pm

Ping-Shou Zhong

Michigan State University

Tests for High-dimensional Covariance Structures

Structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the data (p) is smaller than the sample size (n). However, this assessment becomes quite challenging when p is bigger than n, since the classical likelihood-ratio test is no longer applicable.

Thursday, December 4, 2014 - 3:30pm
Room 306, Statistics

Yichen Qin

University of Cincinnati

Robust Hypothesis Testing via Lq-Likelihood

In this talk, we introduce a robust testing procedure — the Lq-likelihood ratio test (LqLR).  We derive the asymptotic distribution of our test statistic and demonstrate its robustness properties both analytically and numerically.

Thursday, November 20, 2014 - 3:30pm

Weng Kee Wong


Using Animal Instincts to Find Efficient Experimental Designs

Experimental costs are rising and it is important to use minimal resources to make statistical inference with maximal precision. Optimal design theory and ideas are increasingly applied to address design issues in a growing number of disciplines, and they include biomedicine, biochemistry, education, agronomy, manufacturing industry, toxicology and food science, to name a few.  

Thursday, November 13, 2014 - 3:30pm

Jiming Jiang

UC Davis

On High-dimensional Misspecified Mixed Model Analysis in Genome-wide Association Study

We study behavior of the restricted maximum likelihood (REML) estimator under a misspecified linear mixed model (LMM) that has received much attention in recent gnome-wide association studies. The asymptotic analysis establishes consistency of the REML estimator of the variance of the errors in the LMM, and convergence in probability of the REML estimator of the variance of the random effects in the LMM to a certain limit, which is equal to the true variance of the random effects multiplied by the limiting proportion of the nonzero random effects present in the LMM.

Thursday, November 6, 2014 - 3:30pm

Lynne Billard

The University of Georgia

Distributions are the Numbers of the Future - Symbolic Data Analysis

Massively large data sets are routine and ubiquitous given modern computer capabilities. What is not so routine is how to analyse these data. One approach is to aggregate the data sets according to some scientific criteria. The resultant data are perforce symbolic data, i.e., lists, intervals, histograms, and so on. Applications abound, especially in the medical and social sciences. Other data sets (small or large in size) are naturally symbolic valued, such as species data, data with measurement uncertainties, confidential data, and the like.

Thursday, October 16, 2014 - 3:30pm

Christopher Nachtsheim

University of Minnesota

Recent Developments in Definitive Screening

Definitive Screening Designs (DSDs), discovered in 2011, are a new alternative to standard two-level screening designs. There are many desirable features of this family of designs. They require few runs while providing orthogonal main effects and avoiding any confounding of main effects by two-factor interactions. In addition they allow for estimating any quadratic effect of the continuous factors. The two-factor interactions are correlated but not confounded with each other. Moreover, in DSDs with 6 or more factors, it is possible to fit a full quadratic model in any three factors.

Thursday, October 23, 2014 - 3:30pm

Pengsheng Ji

The University of Georgia

Coauthorship and Citation Networks for Statisticians

We collect the coauthor and citation data for all research papers published in four of the top journals in statistics between 2003 and 2012, analyze the data from several different perspectives (e.g., patterns, trends, community structures) and present an array of interesting findings. (1) Both the average numbers of papers per author published in these journals and the fraction of self citations have been decreasing, but the proportion of distant citations has been increasing.

Thursday, October 2, 2014 - 3:30pm

Dennis Lin

Penn State University

Dimensional Analysis and Its Applications in Statistics

Dimensional Analysis (DA) is a fundamental method in the engineering and physical sciences for analytically reducing the number of experimental variables prior to the experimentation.  The principle use of dimensional analysis is to reduce from a study of the dimensions of the variables on the form of any possible relationship between those variables.  The method is of great generality.  In this talk, an overview/introduction of DA will be first given.  A basic guideline for applying DA will be proposed, using examples for illustration.  Some initial ideas on using DA for Data Analysis and

Tuesday, October 7, 2014 - 3:30pm


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