Hao Zhang

Purdue University

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 inferences.

Thursday, September 7, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

Julian L. Parris

JMP

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.

Thursday, March 2, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

Alan Gelfand

Duke University

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 a bounded spatial region crossed with circular time. In addition, crimes are classified by crime type.

Thursday, September 28, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

Shiyu Wang

University of Georgia

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 suboptimal item selection, and the compromise of test validity, due to the potential adoption of deceptive test-taking strategies by the examinees.

Thursday, March 23, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

WenZhan Song

The University of Georgia

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.

Thursday, January 12, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

Donald Richards

Pennsylvania State University

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.

Thursday, January 5, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

Miles Lopes

University of California, Davis

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.

Thursday, April 6, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

Jerry Reiter

Duke University

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 augmented records.

Thursday, March 30, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

Haiying Wang

The University of New Hampshire

Optimal Subsampling for Large Sample Logistic Regression

For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least squares estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities.

Thursday, March 16, 2017 - 3:30pm
Type: 
Room 306, Statistics Building 1130

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