Advances in genetics have allowed scientists to identify genes (biomarkers) that are linked with certain diseases. To translate these great scientific findings into real-world products (personalized medicine) for those who need them, clinical trials play an essential and important role. To develop personalized medicine, we need new designs of clinical trials so that genetics information and other biomarkers can be incorporated in treatment selection.
The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.
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Immunohistochemical (IHC) staining is widely used in the diagnosis of cancer.
Preliminary results are presented from an ongoing study of the development of tertiary students’ reasoning in a one-semester college-level statistics course. The modeling and simulation-based course relies on randomization and bootstrap methods for inference. Students in the statistics course learn to use TinkerPlots® to create "just by chance" models that form the basis of simulated distributions of sample statistics in order to draw an inference about an observed effect or difference.
In model-based survey sampling Hierarchical Bayesian (HB) methods have gained immense popularity. One of the major reasons for this popularity remains the convenience in implementation of HB models using MCMC methods even when the models are complex. An inevitable part of this approach is elicitation of the priors for the parameters involved in the model. Authentic expert information can be incorporated by assigning suitable subjective prior distribution to the parameters.
Space-filling designs are widely used for emulating computer simulators. Over the last three decades, a wide spectrum of Latin hypercube designs (LHDs) has been proposed with different space-filling criteria like minimum correlation among factors, maximin inter-point distance, and orthogonality among the factors. In this talk I will present a new class of space-filling designs. These designs are derived from randomization defining contrast subspaces (RDCSSs) in two-level factorial experiments.
Large genome-wide association studies are now consistently pointing towards an extremely polygenic model for complex diseases. Such models may involve thousands of susceptibility markers, each conferring only a modest risk, but collectively they could be explaining substantial variation in disease-risks in populations. Further, a few large studies of gene-environment interactions indicate that genetic and environmental risk-factors may broadly act in a multiplicative fashion on the risk of a number of different cancers and possibly other diseases.
JMP includes comprehensive capabilities for statistical and graphical analysis of data for every academic field and most research needs. Easily perform basic statistics to the most complex analyses a PhD student may encounter. JMP runs on Windows and Macintosh operating systems and also functions as an easy, point-and-click interface to SAS®, R, MATLAB and Excel.
This dissertation undertakes the theory and methods of sufficient dimension reduction in the content of Hilbert-Schmidt Independence Criterion (HSIC). The proposed estimation methods enjoy model free property and require no link function to be smoothed or estimated. Two tests: Permutation test and Bootstrap test, are investigated to examine the true underlying dimension of data considered. Sampling distribution of our estimator is established in single-index regressions. Root-n consistency of our estimator is proved for multiple-index models.
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.
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 review regression and clustering methods for interval data.