Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Zaid Abdo

Evolution is a complex process that involves many sources of variation and interactions that make mathematical modeling a challenge. In nature, evolution is a time dependent process that involves a large number of environmental variables influencing the adaptation of a population and its progress. Environmental effects include the interaction between a population with its physical environment, its interaction with other populations and species,…


Nat Kulvanich

The times of repeated behavioral events can be viewed as a realization of a temporal point process. Rathbun, Shiffman, and Gwaltney (2007) used a Poisson process (Cox 1972) for modelling repeated behavioral events impacted by time-varying covariates. Taking an inspiration from the techniques of Generalized Linear Mixed Models, and the EM algorithm (Dempster et al. 1977) for finite mixture model estimation, we will further extend their models to…


Jian Huang

TBD Joint seminar with the Department of Epidemiology and Biostatistics.


Peter Hall

If the intensity of light radiating from a star varies in a periodic fashion over time, then there are significant opportunities for accessing information about the star's origins, age and structure. For example, if two stars have similar periodicity and light curves, and if we can gain information about the structure of one of them (perhaps because it is relatively close to Earth, and therefore amenable to direct observation), then we can make…


Alan Dorfman

We propose a method for evaluating the mean square error (mse) of a possibly biased estimator $\hat\Theta_1$, or, rather, the class of estimators to which it belongs. The method uses confidence intervals c of a corresponding unbiased estimator $\hat\Theta$ and makes its assessment based on the extent to which c includes $\hat\Theta_1$. The method does not require an estimate, implicit or explicit, of the bias of $\hat\Theta_1$, is indifferent to…


Marie Davidian (UGA/Clemson Seminar)

A treatment regime is a rule that assigns a treatment, among a set of possible treatment options, to a patient as a function of his/her individual characteristics, hence \personalizing" treatment to the patient. A goal is to identify the optimal treatment regime; that is, the regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, for a…


Dongseok Choi

In medical research, it is often interested in finding subgroups in an outlier group. For example, a certain medical condition can be more frequent in a small group that is different from the majority of population. One approach to find groups in a data set is using cluster analysis. Cluster analysis has been widely used tool in exploring potential group structure in complex data and has received greater attention in recent years due to data…


Xingye Qiao

We study the similarity and differences between two state-of-the-art large margin classifiers DWD and SVM, and propose a unified family of classification machines, the FLexible Assortment MachinE (FLAME), where SVM and DWD are two special cases within the family. The FLAME family helps to understand the connection and differences between SVM and DWD method, and also improves both methods by providing a better tradeoff between imbalance…


Richard Scheaffer

In this International Year of Statistics it is appropriate to review the long history of statistics education at the school level, a history that laid the foundation for the great possibilities that lie before us today. These possibilities can best be made realities through the concerted efforts of the entire statistics community, especially the academic community, in curriculum development and teacher education. Such realities, in turn, can…


Lan Wang

We introduce a quantile regression framework for analyzing high-dimensional heterogeneous data. To accommodate heterogeneity, we advocate a more general interpretation of sparsity which assumes that only a small number of covariates influence the conditional distribution of the response variable given all candidate covariates; however, the sets of relevant covariates may differ when we consider different segments of the conditional distribution.…
Subscribe to Colloquium Series