Tags: Colloquium Series

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


High-dimensional Bayesian Geostatistics

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatial-temporal process models have become widely deployed statistical tools for researchers to better understanding the complex nature of spatial and temporal…


The Big Data Issues in Spatial Statistics

One of areas where big data are collected is in environmental and climate studies. The Global Circulation Models or Regional Circulation Models can generate huge amount of data in space and time. Data collected through remote sensing or sensor networks are also huge. All these data are correlated spatially and temporally. One therefore has to deal with the huge covariance matrix in the traditional likelihood-based inferences or Bayesian…


Beyond Analysis: Teaching Statistics with Modern Statistical Software

Modern statistical software isn’t just a tool to help students analyze data, but through interactive graphics and rich statistical visualization these tools help students learn and engage with core concepts in statistics and data analysis. In this session we will see examples of how to use interactivity of software to aid in the communication of otherwise difficult to grasp concepts in the analysis and visualization of data. Examples…


Space and circular time log Gaussian Cox processes with application to crime event data

We view the locations and times of a collection of crime events as a space-time point pattern. So, with either a nonhomogeneous Poisson process or with a more general Cox process, we need to specify a space-time intensity. For the latter, we need a random intensity which we model as a realization of a spatio-temporal log Gaussian process. In fact, we view time as circular, necessitating valid separable and nonseparable covariance functions over…


Understanding the Effects of Predictor Variables in Black Box Supervised Learning Models

For many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount importance. A shortcoming of black box supervised learning models (e.g., complex trees, neural networks, boosted trees, random forests, nearest neighbors, local kernel-weighted methods, support vector regression, etc.) in this regard is their lack of interpretability or transparency. Partial…


Computerized Adaptive Testing with Response Revision: Design and Asymptotic Theory

In Computerized Adaptive Testing (CAT), questions are selected in real time and are adjusted to the test-taker’s latent ability. While CAT has become popular for many measurement tasks, such as educational testing and patient reported outcomes, it has been criticized for not allowing examinees to review and revise their answers.  Two main concerns regarding response revision in CAT are the deterioration of estimation efficiency, due to…


Fog Computing in Cyber-physical Systems and Security

In this talk, we will discuss research challenges and opportunities of Fog Computing in Cyber-physical Systems and Security and present several case studies. We will first present an innovative Real-time In-situ Seismic Imaging (RISI) system design with fog computing. It is a smart sensor network that senses and computes the 3D subsurface imaging in real-time and continuously. Instead of data collection then post processing, the mesh network…


Parameter estimation for linear Gaussian covariance models

Linear Gaussian covariance models are Gaussian models with linear constraints on the covariance matrix. Such models arise in stochastic processes from repeated time series data, Brownian motion tree models of phylogenetic data and network tomography models used for analyzing connections in the Internet. Maximum likelihood estimation in this class of models leads to a non-convex optimization problem that typically has many local maxima. Using…


Unknown Sparsity in Compressed Sensing: Denoising and Inference

The theory of Compressed Sensing (CS) asserts that an unknown p-dimensional signal can be accurately recovered from an underdetermined set of n linear measurements with n<p, provided that x is sufficiently sparse. However, in applications, the degree of sparsity ||x||_0 is typically unknown, and the problem of directly estimating ||x||_0 has been a longstanding gap between theory and practice. A closely…


A simple way to incorporate prior information on margins in Bayesian latent class models

I present an approach to incorporating informative prior beliefs about marginal probabilities into Bayesian latent class models for categorical data. The basic idea is to append synthetic observations to the original data such that (i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of…
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