International Conference on Statistical Distributions and Applications
ICOSDA 2019

 Oct. 10-12, 2019, at Eberhard Conference Center, Grand Rapids, MI, USA

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Conference Keynote Speakers

David Banks

https://stat.duke.edu/sites/stat.duke.edu/files/styles/people_grid_thumbnail/public/external-images/441a1d0c653540fc1cbc9de693e17ca8.jpg?itok=hcVkv6Tb

banks@stat.duke.edu

Dr. David Banks is currently the Director of the Statistical and Applied Mathematical Sciences Institute, and a professor in the Dept. of Statistical Science at Duke University.  He has held previous positions at UC Berkeley, the University of Cambridge, Carnegie Mellon, the National Institute of Standards and Technology, the US Dept. of Transportation, and the FDA.  He obtained his PhD in 1984 at Virginia Tech, and has served as editor of JASA and Statistics and Public Policy.  He is interested in dynamic text networks, risk analysis, agent-based models, biosurveillance, and human rights data.

Title: Adversarial Risk Analysis

Abstract: Adversarial Risk Analysis (ARA) is a Bayesian alternative to classical game theory.  Rooted in decision theory, one builds a model for the decision-making of one's opponent, placing subjective distributions over all unknown quantities.  Then one chooses the action that maximizes expected utility.  This approach aligns with some perspectives in modern behavioral economics, and enables principled analysis of novel problems, such as a multiparty auction in which there is no common knowledge and different bidders have different opinions about each other. 

Grace Yi


 

yyi@uwaterloo.ca

Dr. Grace Y. Yi is a Professor of Statistics and University Research Chair at the University of Waterloo. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. She is the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018). She was President of the Biostatistics Section of The Statistical Society of Canada in 2016, and the Founder and Chair of the first chapter (Canada Chapter) of The International Chinese Statistical Association. She serves as an Associate Editor for a number of statistical journals. Professor Yi is broadly interested in various areas concerning statistical learning and applications. She published a wide range of research papers in reputable statistical journals, authored a research monograph and co-edited the book Advanced Statistical Methods in Data Science, both published by Springer.

Professor Yi was the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist’s professional accomplishments in research during the first 15 years after having received a doctorate. She was a recipient of the prestigious University Faculty Award granted by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Title: Making Sense of Noisy Data: Some Issues and Discussions

 

Abstract: Thanks to the advancement of modern technology in acquiring data, massive data with diverse features and big volume are becoming more accessible than ever. The impact of big data is significant. While the abundant volume of data presents great opportunities for researchers to extract useful information for new knowledge gain and sensible decision making, big data present great challenges. A very important, sometimes overlooked challenge is the quality and provenance of the data. Big data are not automatically useful; big data are often raw and involve considerable noise. Typically, the challenges presented by noisy data with measurement error, missing observations and high dimensionality are particularly intriguing. Noisy data with these features arise ubiquitously from various fields including health sciences, epidemiological studies, environmental studies, survey research, economics, and so on. In this talk, I will discuss the issues induced from noisy data and how these features may challenge inferential procedures.

Scott Vander Wiel

cid:09631670-A17C-40CA-877A-50484E8DB5D4@lanl.gov

scottv@lanl.gov

Dr. Scott Vander Wiel is a fellow of the American Statistical Association, conducting statistics research at Los Alamos National Laboratory since 2005 and previously at Bell Laboratories since 1991.  He collaborates with engineers and scientists to analyze data and develop statistical methods for problems in diverse areas such as radio astronomy, malware detection, nuclear forensics, power grid uncertainty, rare event estimation, anomaly detection, and numerical optimization.  Scott holds patents on methods for network traffic modeling and for incremental quantile estimation. He won the ASA prize for Outstanding Statistical Applications and the ASQ Frank Wilcoxon prize for the Best Practical Application for a 2002 paper on modeling bandwidth in optical fiber. At LANL he has focused on uncertainty modeling for electric power, streaming radio astronomy analysis, weapons reliability modeling, high explosive surveillance and cyber security. Scott earned a Ph.D. in Statistics from Iowa State University.

Title: Fitting Stress-Strain Fields in Polycrystalline Materials—Statistical Art and Science

 

Abstract: We discuss statistical art and science involved in collaborating to build models for polycrystalline materials using large simulation data sets with the ultimate objective of connecting statistical fluctuations to failure propensity. Quantitative understanding of shock-failure mechanisms is needed to assure reliable performance in extreme environments. I will describe three statistical representations of stress-strain fields in simulated Tantalum.  First, a Gauss-Markov random field on ~1M computational elements is designed to capture stress effects that tend to become more extreme near grain boundaries.  Next, two different regression models are fit to reduced data sets corresponding to grain boundary centroids.  Orientations of crystal lattices are used as predictors in these models and present special challenges because the point group structure of crystal orientations imposes symmetries on the regression problem.  One model regresses on (hyper-) spherical harmonic bases.  The other is a Gaussian process fit utilizing an orientation distance metric.

Patrick Wolfe

 

https://signalprocessingsociety.org/sites/default/files/uploads/images/professional_development/DL/Patrick_Wolfe.jpg

patrick@purdue.edu

Dr. Patrick Wolfe is the Frederick L. Hovde Dean of Science and Miller Family Professor of Statistics & Computer Science at the Purdue University. He received his Ph.D. from the University of Cambridge as U.S. National Science Foundation Graduate Research Fellow. In 2012, he took up an Established Career Fellowship in the Mathematical Sciences at University College London (UCL), where he also served as a Royal Society Research Fellow and as founding Executive Director of UCL’s Big Data Institute. Dr. Wolfe is currently Chair, IEEE SPS Big Data Special Interest Group and serves on the steering committee of the IEEE SPS Data Science Initiative, as well as Co-Chair, Data Science Section of the Institute for Mathematical Statistics. Dr. Wolfe has received awards for his research from a number of international bodies, including the Royal Society, the Acoustical Society of America, and the IEEE. He is active in the global mathematics, statistics, and physical sciences communities. He was an organizer and Simons Foundation Fellow at the Isaac Newton Institute for Mathematical Sciences 2016 semester research program on Theoretical Foundations for Statistical Network Analysis.

Title: Statistical Distributions and Network Testing

 

Abstract: How do we draw sound and defensible conclusions from big data, for example in comparing two sets of observations, or evaluating goodness of model fit? In this talk I will discuss the current state of the art in one area of particular interest: big network data.  Progress in this area includes the development of new large-sample theory that helps us to view and interpret networks as statistical data objects, along with the transformation of this theory into new statistical methods to model and draw inferences from network data in the real world. The insights that result from connecting theory to practice also feed back into pure mathematics and theoretical computer science, prompting new questions at the interface of combinatorics, analysis, probability, and algorithms.

Conference Plenary Speakers

Susmita Datta

Datta, Susmita_in red

susmita.datta@ufl.edu

Dr. Susmita Datta is Professor at University of Florida (UF), Department of Biostatistics. She is the Co-Director of the Biostatistics, Epidemiology and Research Design Program (BERD) of UF Clinical and Translational Science Institute. Dr. Datta is widely (>100) published in peer reviewed journals. Her work has been continuously funded by the National Science Foundation and the National Institutes of Health. She is a fellow of the American Statistical Association (ASA), an elected member of the International Statistical Institute (ISI), and fellow of the American Association for the Advancement of Science (AAAS). Her research area includes bioinformatics, genomics, proteomics, metabolomics, lipidomics, clustering and classification techniques, infectious disease modeling, statistical issues in population biology, systems biology, survival analysis, multi-state models and big data analytics. She has recently published a book on “Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry” by Springer.

Professor Datta is enthusiastic in promoting women in STEM fields and has served as President of Caucus for Women in Statistics (CWS) and is presently appointed to the Committee of Women in Statistics of ASA (COWIS). She is the founding executive committee member of the Women in Statistics and Data Science conference (WSDS).

Title: Advances and Challenges in Single Cell RNA-Seq Analysis

 

Abstract: Traditionally, transcriptomic studies have examined transcript abundance measurements averaged over bulk populations of thousands (or even millions) of cells. While these bulk RNA-sequencing (RNA-Seq) measurements have been valuable in countless studies, they often conceal cell-specific heterogeneity in expression signals that may be paramount to new biological findings. Fortunately, with single cell RNA-sequencing (scRNA-Seq), transcriptome data from individual cells are now accessible, providing opportunities to investigate functional states of cells, identify rare cell populations and uncover diverse gene expression patterns in cell populations that seem homogeneous. However, there are challenges in analyzing such scRNA-Seq data. Amongst many challenges the most significant are the bimodal or multimodal distribution, sparsity and tremendous heterogeneity in the data. Consequently, we will describe potential ways of statistical modeling of such data, finding differentially expressed genes and possible ways of constructing gene-gene interaction network using this data.  Moreover, we will compare the performance of our modeling and differential analysis with respect to some other existing methods.

Kimberly Sellers

 

kfs7@georgetown.edu

Kimberly Sellers, Ph.D. is an Associate Professor of Mathematics and Statistics, specializing in Statistics at Georgetown University in Washington, DC; and a Principal Researcher with the Center for Statistical Research and Methodology Division of the U.S. Census Bureau. Prof. Sellers held previous faculty positions at Carnegie Mellon University as a Visiting Assistant Professor of Statistics, and the University of Pennsylvania School of Medicine as an Assistant Professor of Biostatistics and Senior Scholar at the Center for Clinical Epidemiology and Biostatistics before her return to the DC area. Her research areas of interest and expertise center on generalized statistical methods involving count data that contain data dispersion for which she became an Elected Member of the International Statistical Institute in 2018. Meanwhile, Sellers is an active contributor to efforts to diversify the fields of mathematical and statistical sciences, both with respect to gender and race/ethnicity. She was the 2017-2018 Chairperson for the American Statistical Association’s Committee on Women in Statistics, and is an Advisory Board member for the Black Doctoral Network.

Title: Flexible Regression Models for Dispersed Count Data

 

Abstract: Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications across disciplines. Real data, however, are often over- or under-dispersed relative to the Poisson model, and thus are not conducive to Poisson regression. This talk presents a regression model based on the Conway-Maxwell-Poisson (COM-Poisson or CMP) distribution, which serves as a flexible alternative that contains both the Poisson and logistic regressions as special cases, and can handle other count data with a range of dispersion levels. We discuss model estimation, inference, diagnostics etc. for both the standard CMP regression and its zero-inflated analog, and introduce the associated R package, COMPoissonReg, developed to aid analysts with such data.

John Preisser

Preisser,John

jpreisse@bios.unc.edu

 

Dr. John S. Preisser is Professor of Biostatistics in the Gillings School of Global Public Health at the University of North Carolina (UNC) at Chapel Hill and a Fellow of the American Statistical Association. He is also Deputy Director of Biostatistics, Epidemiology and Research Design in the North Carolina Translational & Clinical Sciences Institute. His primary interests include categorical data analysis, cluster randomized trials, correlated binary data, estimating equations for marginal mean regression models, longitudinal data, marginalized mixture regression models and statistical methods for handling missing data. He teaches a course in Categorical Data Analysis to biostatistics doctoral students at UNC and has over 50 co-authored papers in biostatistics and statistics journals.

Professor Preisser is also interested in the innovative application and dissemination of state-of-the-art statistical methods in diverse areas of human health and welfare including clinical trials, dentistry, epidemiology, health services research, and the long-term care of the elderly in nursing homes and other institutionalized settings.  For over twenty years, he has contributed to the design and analysis of scores of multi-disciplinary health sciences studies that have been published in over 150 research articles.

 

Title:

 

Abstract: Due to logistical or costs constraints, correlated binary outcomes may be recorded as partially-sampled clusters where some observations within clusters are intentionally missing. In this context, we discuss estimation of the cell probability for the cross-classification of zeros in a complete table of multivariate binary data. The beta-binomial model of within-cluster exchangeability is used to estimate disease prevalence where disease is defined as the presence of one or more observations (sites) affected with the binary condition in a complete cluster, -the complement of the zero cell. When the propensity for the condition varies across sites and pairwise correlations follow a spatial clustering model, alternative prevalence estimators are derived under a conditional linear family of multivariate Bernoulli distributions.  Properties of the estimators are investigated. Estimators of periodontitis prevalence using random partial-mouth samples in oral epidemiological research are illustrated including those for alternative definitions of disease, e.g., two or more sites affected.

Paul Gustafson

gustaf@stat.ubc.ca

Dr. Paul Gustafson is a Professor in the Department of Statistics at the University of British Columbia.  He obtained his Ph.D. in Statistics from Carnegie Mellon University in 1994.  His current research interests include Bayesian methods, causal inference, evidence synthesis, measurement error models, and partial identification.  Much of his work is motivated by epidemiological and public health applications.  Professor Gustafson is the author of two research monographs, in the areas of measurement error models (2004) and Bayesian inference under partial identification (2015) respectively.  He is currently the Special Editor for Statistical Methods for Epidemiology, and he is a former Editor-in-Chief of the Canadian Journal of Statistics.  He was the 2008 recipient of the CRM-SSC Prize, and he was named a Fellow of the American Statistical Association in 2011.

Title: Limiting posterior distributions from partially identified models: How do they arise, and what do they tell us?

 

Abstract: Understanding what distributions can arise in various large-sample limiting senses has a rich statistical tradition.  In keeping with this, one can investigate the large-sample limit of a posterior distribution when the underlying statistical model is only partially identified.   This talk will have three aims.   First, we point out that partially identified models arise quite naturally in many observational study settings.   Second, we describe how the limiting posterior distribution is determined, for a given partially identified model and given choice of prior distribution.   Third, we consider what the limiting distribution, through both its support and its shape, tells us about the scenario at hand.