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Slideshow

Tags: Colloquium Series

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

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. This has hindered their use in contemporary high dimensional data analysis, where the…
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…
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…
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…
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…
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…
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…
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…
Functional magnetic resonance imaging (fMRI) is one of the leading brain mapping technologies for studying brain activity in response to mental stimuli. For neuroimaging studies utilizing this pioneering technology, there is a great demand of high-quality experimental designs that help to collect informative data to make precise and valid inference about brain functions. In this talk, I provide a survey on some recently developed analytical and…
In this talk we will give a quick overview of some of the strengths and challenges in Bayesian variable selection as it evolved over the last two decades. We will then discuss two specific problems in linear regression with strong multicollinearity among the covariates. A variety of Markov chain Monte Carlo algorithms  have been proposed in the literature for Bayesian variable selection in linear regression. The computation for these…

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