Tags: Colloquium Series

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


Colloquium: Jingyi Jessica Li

Neyman-Pearson Classification Algorithms and NP Receiver Operating Characteristics In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural…


Cun-Hui Zhang

Semi-Low-Dimensional Inference Via Bias Correction  We consider statistical inference in a semi-low-dimensional approach to the analysis of high-dimensional data. The relationship between this semi-low-dimensional approach and regularized estimation of high-dimensional objects is parallel to the more familiar one between semi-parametric analysis and nonparametric estimation. Low-dimensional projection methods are used to correct the bias of…


Statistical Methods with Applications in Epigenomics, Metagenomics and Neuroimaging

As the rapid development of biotechnology, more complex data sets are now generated to address extremely complex biological problems. It is challenging to develop new statistical methods to analyze such data. In this thesis, I propose a nonparametric hypothesis test and two statistical learning methods to solve biological problems arising from epigenomics, metagenomics, and neuroimaging. First, the proposed test aims at testing the significance…


Nonparametric Methods for Big and Complex Datasets Under a Reproducing Kernel Hilbert Space Framework

Large and complex data have been generated routinely from various sources, for instance, time course biological studies and social media. Classic nonparametric models, such as smoothing spline ANOVA models, are not well equipped to analyze such large and complex data. To overcome these challenges, I propose novel nonparametric methods under a reproducing kernel Hilbert space framework to (1) significantly reduce daunting computational costs of…


Dimension Reduction and Multisource Fusion for Big Data with Applications in Bioinformatics

With the rapid development of technology, increasing amount of data has been produced from many fields of science, such as biology, neuroscience, and engineering. The inadequate sample is no longer a bottleneck of modern statistical research. More often, we are facing data of extremely high dimensionality or coming from remarkably different sources. How to effectively extract information from the large-scale and high-dimensional data or data…


Optimal Designs for a Panel Mixed Logit Model

We discuss optimal designs for the panel mixed logit model. The panel mixed logit model is usually used for the analysis of discrete choice experiments. The information matrix used in design criteria does not have a closed form expression and it is computationally difficult to evaluate the information matrix numerically. We derive the information matrix and use the obtained form to propose three methods to approximate the information matrix. The…


A time-varying AR, bivariate DLM of functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a relatively new neuroimaging technique. It is a low cost, portable, and non-invasive method to measure brain activity via the blood oxygen level dependent signal. Similar to fMRI, it measures changes in the level of blood oxygen in the brain. Its time resolution is much finer than fMRI, however its spatial resolution is much courser--similar to EEG or MEG. fNIRS is finding widespread use on young…


Unsupervised Self-Normalized Change-Point Testing for Time

We propose a new self-normalized method for testing change points in the time series setting. Self-normalization has been celebrated for its ability to avoid direct estimation of the nuisance asymptotic variance and its flexibility of being generalized to handle quantities other than the mean. However, it was developed and mainly studied for constructing confidence intervals for quantities associated with a stationary time series, and its…


William R. Myers

"Industry Day" https://www.linkedin.com/in/bill-myers-05425815/


Big Geospatial-temporal Data and Analytics as-a-Service

The rapid growth of geospatial-temporal data sources from satellites, drones, weather modeling, IoT sensors etc., accumulating at a pace of petabytes to exabytes annually, opens unprecedented opportunities for industrial applications. However, the sheer size and complexity of the data also raises considerable challenges for conventional tools, relational geospatial databases, and cloud geospatial data services based on file systems (manifested…
Subscribe to Colloquium Series