Vladimir Dragalin

Janssen Pharmaceuticals, Johnson and Johnson

Adaptive Designs for Population Enrichment Strategy

Population enrichment strategy offers a specific adaptive design methodology to study the effect of experimental treatments in various sub-populations of patients under investigation.  Instead of limiting the enrollment only to the enriched population, these designs enable the data-driven selection of one or more pre-specified subpopulations at an interim analysis and the confirmatory proof of efficacy in the selected subset at the end of the trial.

Thursday, February 18, 2016 - 12:30pm
Cohel Room, Statistics Building

Ming Hu

New York University

A hidden Markov random field based Bayesian method for the detection of long-range chromosomal interactions in Hi-C Data

Motivation: Advances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, detecting non-random interactions between loci from the two-dimensional contact frequency matrix.

Thursday, September 10, 2015 - 12:00pm

Shuangge Steven Ma

Yale University

Robust Network-based Analysis of the Associations between (Epi)Genetic Measurements

Multiple types of (epi)genetic measurements are involved in the development and progression of complex diseases. Different types of (epi)genetic measurements are interconnected, and modeling their associations can lead to a better understanding of disease biology and facilitate building clinically useful models. Such analysis is challenging in multiple aspects. To fix notations, we use gene expression (GE) and copy number variation (CNV) as an example. Both GE and CNV measurements are high-dimensional.

Tuesday, October 6, 2015 - 3:30pm

Howard Bondell

North Carolina State University

Tales of Multiple Regression: Informative Missingness, Recommender Systems, and R2-D2

In this talk, I discuss two current projects tangentially related under the umbrella of regression.

Thursday, March 24, 2016 - 3:30pm

Subhashis Ghoshal

North Carolina State University

Bayesian Clustering of Functional Data Using Local Features

Functional data arise frequently especially in today’s big data regime in diverse contexts including patient monitoring in medical treatments, weather analysis and in general, in everything that produces observations nearly continuous in time. Clustering of data is a fundamental tool in understanding similarities and dissimilarities between units in the data.

Thursday, March 3, 2016 - 3:30pm

Jae-Kwang Kim

Iowa State University

Bottom-up estimation and top-down prediction in multi-level models: Solar Energy Prediction combining information from multiple sources

Accurately forecasting solar power using a statistical method from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements from an automated monitoring network so as to better predict solar power in a timely manner. To this end, we propose a bottom-up approach of analyzing large-scale multilevel models with great computational efficiency requiring minimum monitoring and intervention.

Tuesday, February 23, 2016 - 3:30pm

Amy Froelich

Iowa State University

Developing and Using Electronic Assessments to Inform Student Learning and Instruction in Introductory Statistics

As a part of a multi-year NSF funded project (DUE 12-45504), we developed electronic vocabulary, clicker and homework assessments at the topic level for a broad range of introductory statistics courses. In this talk, I will discuss the development and structure of these assessments, including the selection of topics, specification of learning outcomes and vocabulary words, and principles used in the development of the assessment questions. I will then describe some of the research on student learning from this work and its impact on instruction in our introductory statistics course.

Thursday, March 31, 2016 - 3:30pm

Soumen Lahiri

NC State University

A Frequency Domain Empirical Likelihood Method for Irregularly Spaced Spatial Data

In this talk, we consider empirical likelihood methodology for irregularly spaced spatial data in the frequency domain. The main result of the paper shows that upto a suitable (and nonstandard) scaling, Wilk’s phenomenon holds for the logarithm of the empirical likelihood ratio in the sense that it is asymptotically distribution free and has a chi-squared limit. As a result, the proposed spatial FDEL method can be used to build nonparametric, asymptotically correct confidence regions and tests for a class spectral parameters that are defined through spectral estimating equations.

Thursday, April 28, 2016 - 3:30pm

Yao Xie

Georgia Institute of Technology

 Yao Xie joined Georgia Institute of Technology as an Assistant Professor in the H. Milton Stewart School of Industrial & Systems Engineering in 2013. Prior to that, she worked as a Research Scientist at Duke University in the Department of Electrical and Computer Engineering, after receiving her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2011. She is interested in signal processing, sequential analysis (e.g.

Thursday, September 3, 2015 - 3:30pm


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