## Peter Hall

UC Davis & U. Melbourne

Nonparametric Methods for Estimating Periodic Functions, With Applications in Astronomy

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 deductions about the structure of the other. Therefore period lengths, and light-curve shapes, are of significant interest.

Friday, April 12, 2013 - 4:30pm
Type:
Room K/L, Georgia Center

## Alan Dorfman

Bureau of Labor Statistics

A Coverage Approach to Evaluating Mean Square Error

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 the bias/variance breakdown of $\hat\Theta_1$’s mse, and does not require surety of the model on which $\hat\Theta_1$ is based.

Thursday, April 4, 2013 - 3:30pm
Type:

## Marie Davidian (UGA/Clemson Seminar)

A Robust Method for Estimating Optimal Treatment Regimes

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.

Thursday, March 28, 2013 - 4:30pm
Type:
M-101 Martin Hall, Clemson University

## Dongseok Choi

Detecting Subclusters in Outliers

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 mining and high dimensional data such as microarrays.

Thursday, March 7, 2013 - 3:30pm
Type:

## Xingye Qiao

Flexible High-dimensional Classification

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 sensitivity and high dimensional data piling. Several asymptotic properties of the FLAME classifiers are investigated.

Thursday, February 28, 2013 - 3:30pm
Type:

## Richard Scheaffer

Statistics Education K-14: Appreciating the Past, Actuating the Present, and Anticipating the Future

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.

Thursday, February 21, 2013 - 3:30pm
Type:

## Lan Wang

Quantile Regression Analysis of High-dimensional Data

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. In this framework, we investigate the methodology and theory of nonconvex penalized quantile regression.

Thursday, February 14, 2013 - 3:30pm
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## April Galyardt

Discovering Strategy Switching with Mixed Membership

There is ample evidence that in a wide variety of settings, as people try to solve problems they switch strategies. For example, a student taking a test might solve one arithmetic problem using one method and then use a different method on the next problem. More importantly experts use different strategies than novices. Both experts and novices switch strategies, but experts use a different mixture of strategies than novices. Existing psychometric models do not model strategy usage,and cannot capture this critical dimension of expert-novice differences.

Thursday, February 7, 2013 - 3:30pm
Type:

## Andrew Lawson

Medical University of South Carolina

Space-Time Latent Structure health Models with Environmental Covariate linkage

Latent structure models can be developed for the mean level of a space-time count data observation process. The focus is on small area health outcomes observed in fixed spatial units and fixed time periods. We assume a Poison data level model with mean parameterized as a weighted mixture of temporal components. Each area has a distribution of weights assigning the area to a component. The model development is within the Bayesian paradigm, and we make a set of different choices for prior distributions for the weights and temporal components.

Thursday, January 24, 2013 - 3:30pm
Type:

## Zhigen Zhao

Optimal Multiple Testing Procedure Under Linear Models

This paper considers the problem of optimal false discovery rate control under the linear model $Y = X \beta + \epsilon$ , where $\epsilon \sim N(0, sigma^2 I )$. It is an extension of the normal mean model with arbitrary dependence. To solve the problem, we first propose an adjusted z-surrogate which simplifies the original data and captures the useful information. We show that many commonly used surrogates based on univariate associations are biased and inefficient.

Thursday, January 17, 2013 - 3:30pm
Type: