Tags: Colloquium Series

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


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…


April Galyardt

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…


Andrew Lawson

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…


Zhigen Zhao

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…


Ashley Askew

Empirical likelihood is a nonparametric method based on a data-driven likelihood. The flexibility of empirical likelihood facilitates its use in complex settings, which can in turn create computational challenges. Additionally, the Empty Set Problem (ESP) which arises with the Empirical Estimating Equations approach can pose problems with estimation, as data are unable to meet constraints when the true parameter is outside the convex hull. As an…


Donald Richards

We consider problems in statistical inference with two-step, monotone incomplete data drawn from a multivariate normal population. We derive stochastic representations for the distributions of the maximum likelihood estimators of the population mean vector and covariance matrix and obtain results for inference on the mean vector and covariance matrix, including: lower bounds on the level of confidence associated with ellipsoidal confidence…


Yuan Xue

Sufficient dimension folding is the technology to reduce the dimensions of matrix- or array-valued objects as well as keep their data structure.  In this talk, I consider the sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. I propose the concept of central mean folding subspace and its two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded…
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