Brian Williams

Los Alamos National Lab

2017 Joint UGA Clemson Colloquium

Gradient-Free Construction of Active Subspaces for Dimension Reduction

Authors: Brian J. Williams (LANL), Allison Lewis (Johns Hopkins University), Ralph C. Smith (NCSU), Max Morris (ISU), Bassam Khuwaileh (Univ. of Sharjah)

Thursday, April 13, 2017 - 3:30pm
Clemson University

Gauri Datta

The University of Georgia

Small Area Estimation with Uncertain Random Effects

Random effects models play an important role in model-based small area estimation. Random effects account for any lack of fit of a regression model for the population means of small areas on a set of explanatory variables. In Datta, Hall and Mandal (2011, JASA), we showed that if the random effects can be dispensed with through a statistical test, then the model parameters and the small area means can be estimated substantially accurately. This work is most useful when the number of small areas, m, is moderately large.

Thursday, September 15, 2016 - 3:30pm
Room 306, Statistics Building 1130

Gang Li

University of California, Los Angeles

Prediction Summary Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data

The R-squared statistic, or coefficient of determination, is commonly used to measure the predictive power of a linear model.  It is interpreted as the fraction of variation in the response explained by the predictors. Despite its popularity, a direct equivalent measure is not available for nonlinear regression models and for right-censored time-to-event data. In this talk, I will show that in addition to a measure of explained variation, another measure of explained prediction error is required to assess the predictive power of a nonlinear model.

Thursday, October 27, 2016 - 3:30pm
Room 306, Statistics Building 1130

Bin Nan

University of Michigan

Regression with Covariate Subject to Limit of Detection

We consider generalized linear regression with left-censored covariate due to the lower limit of detection. The complete case analysis by eliminating observations with values below limit of detection yields valid estimates for regression coefficients, but loses efficiency. Substitution methods are biased; and maximum likelihood method relies on parametric models for the unobservable tail probability, thus may suffer from model misspecification.

Thursday, September 29, 2016 - 3:30pm
Room 306, Statistics Building 1130

Vijay Nair

Wells Fargo and University of Michigan

Risk Analysis in Banking: Opportunities and Challenges for Statisticians

Data-based decision making has always been a fundamental part of banking and finance. This has become even more so after the 2008 crisis and the heightened regulatory environment. In this presentation, I will describe the role of statistics in risk modeling and management in large banks, covering model development and model assessment. The talk will give a glimpse into different types of data structures, computing/data platforms used for big data, types of models, and how they are developed and used.

Thursday, October 20, 2016 - 3:30pm
Room 306, Statistics Building 1130

Haonan Wang

Colorado State University

Statistical Analysis of Big Data and Structured Data with Application to Neuroscience

In this talk, we consider two types of data from neuroscience: neuromorphology data and neuron activity data. First, we focus on  data extracted from brain neuron cells of rodents and model each neuron as a data object with topological and geometric properties characterizing the branching structure, connectedness and orientation of a neuron. We define the notions of topological and geometric medians as well as quantiles based on newly-developed curve representations.

Thursday, November 17, 2016 - 3:30pm
Room 306, Statistics Building 1130

Christopher Breen

Eli Lilly and Company

Extreme Quantile Determination for Manufacturing Process Parameters

Monitoring the control and capability of process parameters is a continual and mammoth task for today’s manufacturers. The importance of simple, efficient, and automated approaches cannot be overstated. Paramount in this endeavor is the determination of extreme quantiles. I will review approaches for determining these quantile from the last 25 years of literature, as well as current usage at Eli Lilly and Company. A number of candidate approaches will be carried forward into a simulation to look at their performance against a variety of distributions.

Thursday, November 10, 2016 - 3:30pm
Room 306, Statistics Building 1130

Zhezhen Jin

Columbia University

Statistical issues and challenges in biomedical studies

In this talk, I will present statistical issues and challenges that I have encountered in my biomedical collaborative studies of item selection in disease screening, comparison and identification of biomarkers that are more informative to disease diagnosis, and estimation of weights on relatively importance of  exposure variables on health outcome. After a discussion on the issues and challenges with real examples, I will review available statistical methods and present our newly developed methods.

Thursday, November 3, 2016 - 3:30pm
Room 306, Statistics Building 1130

Ery Arias-Castro

University of California, San Diego

Distribution-free Multiple Testing

We study a stylized multiple testing problem where the test statistics are independent and assumed to have the same distribution under their respective null hypotheses. We first show that, in the normal means model where the test statistics are normal Z-scores, the well-known method of (Benjamini and Hochberg, 1995) is optimal in some asymptotic sense. We then show that this is also the case of a recent distribution-free method proposed by Foygel-Barber and Candes (2015).

Thursday, October 6, 2016 - 3:30pm
Room 306, Statistics Building 1130

Xiaoyu Li

Auburn University

Testing of Regression Functions with Data Missing at Random

This talk includes two testing problems of regression functions with responses missing at random. One problem is minimum distance model checking. The proposed lack-of-fit tests are based on a class of minimum integrated square distances between a kernel type estimator of a regression function and the parametric regression function being fitted. These tests are shown to be consistent against a large class of fixed alternatives. The corresponding test statistics are shown to have asymptotic normal distributions under the null hypothesis.

Thursday, August 25, 2016 - 3:30pm
Statistics Building Room 306


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