Tags: Colloquium Series

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


Takashi Owada

Takashi Owada is an assistant professor of Department of Statistics in Purdue University. Prior to joining Purdue, he was a postdoc researcher at Technion-Israel Institute of Technology, and received his PhD in Operations Research at Cornell University. His research interests lie in random topology, topological data analysis, random graph theory, heavy tail probability, extreme value theory, and …


Zhao Ren

Robust Estimation under Huber's Contamination Model This talk describes some new challenges and results in high-dimensional and nonparametric statistics under the celebrated Huber’s contamination model. We particularly focus on the influence of contamination on the minimax rates and the corresponding rate-optimal procedures. The first part of the talk focuses on robust covariance matrix estimation. To deal with modern complex data sets, not only…


Lingling An

Mediation Analysis of High-Dimensional Microbiome and Host Genome Data Recent studies have shown that human microbiota has a strong impact on human health and disease. However, they mainly focus on the association between human microbiota and the related diseases/health. In this research, we are interested in understanding how the microbiome and host genome jointly impact human health and disease by integrating multiple –omics data through…


Amy Willis

Estimating diversity and relative abundance in microbial communities High-throughput sequencing has advanced our understanding of the role that bacteria and archaea play in marine, terrestrial and host-associated health. Microbial community ecology differs in many ways from macroecology, and therefore new statistical methods are required to analyze microbiome data. In this talk I will present two new statistical methods for the analysis of…


Bodhi Sen

Nonparametric Estimation of a Two-component Mixture Model with application to Multiple Testing    We consider estimation and inference in a two-component mixture model where the distribution of one component is completely unknown. We develop methods for estimating the mixing proportion and the unknown distribution nonparametrically, given i.i.d. data from the mixture model. We use ideas from shape restricted function estimation…


Michael Lavine

Assessing Procedures vs Assessing Evidence Many statistical analyses are characterized by how often a procedure works: how often an interval covers a true value, a null hypothesis is rejected, an item is correctly classified, etc. But assessing how often a procedure works differs from assessing the evidence in a data set. Understanding the difference is prerequisite to understanding what matters in a given analysis: the procedure, the evidence,…


Jungjin Lee

Developing an Web-based Dynamic Graphical Software for Statistics Education, eStat Recent advance in IT and network technology has enabled to develop the statistical packages such as SAS, SPSS and R for mass data processing. However, these well-known packages have not paid their attention to develop a module for statistics education. Many individual developers have developed software for statistics education, but the most of them are limited to…


Colloquium: Yajun Mei

A Multi-Armed Bandit Approach for Online Monitoring High-Dimensional Streaming Data We investigate the problem of online monitoring high-dimensional streaming in resources constrained environments, where one has limited capacity in data acquisition, transmission or processing, and needs decide how to smartly observe which local components or features of high-dimensional streaming data at each and every time so as to detect potential anomaly…


Colloquium: Ryan Martin


Colloquium: L. Jason Anastasopoulos

Regularization Adjusted Local Average Treatment Estimation for Regression Discontinuity Designs The regression discontinuity design is one of the most popular and credible methods available for causal inference with observational data.   Estimation of local average treatment effects in RDDs are typically based on local linear regressions using the outcome variable, a treatment assignment variable, and a continuous running variable.…
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