Tags: Colloquium Series

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


Ming Hu

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…


Shuangge Steven Ma

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…


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. The first part of the talk investigates informative missingness in the framework of recommender systems. For example, in 2009, Netflix ran a $1M prize competition to improve their algorithm to recommend movies to their viewers. In this setting, we can imagine a potential rating for every object-user pair. For Netflix, the object would be the movie…


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.  Bayesian methods for clustering of functional data use models which imply the…


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…


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…


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,…


Yao Xie

 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,…


Liang Liu

http://faculty.franklin.uga.edu/lliu/


Anindya Roy

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…
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