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
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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
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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
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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
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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
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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
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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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Anindya Roy

University of Maryland Baltimore County

Estimation of Vector Autoregressive Moving Average Under Causality and Invertibility Constraints

We present a reparameterization of vector autoregressive moving average (VARMA) models that allows estimation of parameters under the constraints of causality and invertibility. The parameter constraints associated with a causal invertible VARMA model are highly complex. An m-variate VARMA(p; q) process contains (p+q)m2 + m(m+1)/2 parameters, which must be constrained to a complicated subset of the Euclidean space in order to guarantee causality, invertibility. The main result of the paper is a bijection from the constrained set to the entire Euclidean space.

Thursday, October 22, 2015 - 3:30pm
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