Genetic association studies often evaluate the combined group-wise effects of rare and common genetic variants on phenotypes at gene level. Many approaches have been proposed for group-wise association tests, such as the widely used burden tests and sequence kernel association tests with sequencing data. Most of these approaches focus on identifying mean effects. As the genetic associations are complex, we propose an efficient integrated rank test to investigate the genetic effect across the entire distribution/quantile function of a phenotype. The resulting test complements the mean-based analysis and improve efficiency and robustness. The proposed test integrates the rank score test statistics over quantile levels while incorporating Cauchy combination test scheme and Fisher’s method to maximize the power. It generalized the classical quantile-specific rank-score test. Using simulations studies and real Metabochip data on lipid traits, we investigated the performance of the new test in comparison with the burden tests and sequence kernel association tests in multiple scenarios. This is joint work with Tianying Wang and Iuliana Ionita-Laza.

Bio: Ying Wei is a statistician and a Professor of Biostatistics in the Columbia University Mailman School of Public Health, working primarily on quantile regression, semiparametric models of longitudinal data, and their applications.

Wei earned her Ph.D. in Statistics from the University of Illinois at Urbana–Champaign in 2004 and has been a faculty member of Biostatistics at Columbia University, and also an affiliated member of the Data Science Institute ever since.

In 2011, Wei received the Noether Young Scholar Award of the American Statistical Association, “for outstanding early contributions to nonparametric statistics.” In 2015, Wei was elected as a Fellow of the American Statistical Association. Wei is also an elected member of the International Statistical Institute. In 2020 she was named as a Fellow of the Institute of Mathematical Statistics “for contributions to the development, dissemination, and application of mathematical statistics”.

Expectation function-based decision and game theoretic models require both computation of the utility function and its optimization. This can be computationally challenging especially in cases with continuous and multi-modal sources of uncertainty or complex objective function surfaces. We propose augmented simulation approaches that treat the decision variable(s) as random and construct an augmented distribution in the space of both decisions and random variables. Simulation from this distribution simultaneously solves for the expectation of the objective function and optimization problem. In doing so, we sample more frequently from the marginal decision space in that the objective function has higher values in a maximization problem. This talk introduces augmented probability simulation and its extensions to solve for stochastic programming problems and game theoretic models. There will be a discussion and illustration on a variety of applications such as news-vendor type models, service systems and cybersecurity. 

Bio: Dr. Tahir Ekin is an Associate Professor of Quantitative Methods in McCoy College of Business, Texas State University. His research interests include analytical applications in health care fraud assessment and decision modeling under uncertainty. His book on health care fraud analytics titled “Statistics and Health Care Fraud: How to Save Billions” has been recently published. His work has appeared in a variety of journals including Journal of the Royal Statistical Society Series C, International Statistical Review, Naval Research Logistics and Decision Analysis among others. Dr. Ekin holds a Ph.D. in Decision Sciences from The George Washington University, and a B.S. in Industrial Engineering from Bilkent University, Turkey. He is an elected member of International Statistical Institute and currently serves as Vice President of the International Society of Business and Industrial Statisticians.

The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive Vector AutoRegressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is “simplest” in a certain sense. A user-specified strongly convex penalty is used to measure model simplicity, and that same penalty is then used to define an estimator that can be efficiently computed.  We establish consistency of our estimators in a double- asymptotic regime.  Our non-asymptotic error bound analysis accommodates both model specification and parameter estimation steps, a feature that is crucial for studying large-scale VARMA algorithms and is largely unexplored in existing literature.  Our analysis also provides new results on infinite-order VAR, elastic net estimation under a singular covariance structure of regressors, and new concentration inequalities for quadratic forms of random variables from Gaussian time series, which may be of independent interest.  We illustrate the advantage of our method over VAR alternatives on simulated and real data examples.

The department of Statistics at the University of Connecticut is honored to announce the 25th Distinguished Statistician Colloquium. The Pfizer colloquium series ran from 1978 until 2012 and was renewed in 2018. The colloquium series featured C. R. Rao, Bradley Efron, D.R. Cox, Grace Wahba and many more. For a complete list, see https://stat.uconn.edu/pfizer-colloquium/. The purpose of the Colloquium is to provide a forum for a distinguished statistician to share and disseminate their unique perspective and work in the theory and/or application of statistics. Starting from 2018, the series has been co-sponsored by Pfizer, the American Statistical Association, and the Department of Statistics at the University of Connecticut. 

This year’s speaker at the 25th Pfizer Distinguished Statistician Series will be Professor Emerita Nan Laird from Harvard T.H. Chan School of Public Health. This year colloquium will be virtual and will take place on Wednesday, October 14^th, 2020 from 3:00-5:00pmEST. The interview will be conducted by Christoph Lange (Harvard T.H. Chan School of Public Health) and Joseph Hogan (Brown University). 

The lecture and the interview are open to the public but pre-registration will be needed for a link to the webcast.  The event registration website is:   https://uconnuecs.cvent.com/Pfizer2020   

If you have questions, please contact, Juliet Kapsis at juliet.kapsis@uconn.edu or conferences@uconn.edu. 

We thank Pfizer and the ASA for their generous financial support. We also thank the members of the selection committee – Dan Meyer and Demissie Alemayehu from Pfizer, Ron Wasserstein and Nancy Flournoy from the ASA, and Dipak Dey (Chair), Joseph Glaz and Ming-Hui Chen from UConn. Professor Chen also represents the New England Statistical Society (NESS).