Fellow, Prof. Ding-Geng Chen
University of North Carolina-Chapel Hill, USA
Dr. (Din) Ding-Geng Chen is the Wallace H. Kuralt distinguished professor at the School of Social Work and a professor in biostatistics at the Department of Biostatistics at Gillings School of Global Public Health from the University of North Carolina-Chapel Hill, USA. He is also an extraordinary professor in the Department of Statistics at the University of Pretoria, South Africa. Before this, Dr. Chen was a professor in biostatistics at the University of Rochester Medical Center, and the Karl E. Peace endowed eminent scholar chair and professor in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is an elected fellow of the American Statistical Association (ASA), an elected member of the International Statistics Institute (ISI), and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. He has more than 200 professional publications and co-authored/co-edited 30 books on biostatistics clinical trials, biopharmaceutical statistics, interval-censored survival data analysis, meta-analysis, public health statistics, statistical causal inferences, statistical methods in big-data sciences, and Monte-Carlo simulation-based statistical modeling. He has been invited internationally to give scientific speeches, short courses, and tutorials at various scientific conferences.
Speech Title: "Stochastic Cusp Catastrophe Model and its Bayesian Computation"
Abstract: Within the catastrophe theory, cusp catastrophe model is the most used in mathematical and statistical modeling because of its capability to characterize both rational and irrational behavioral processes simultaneously. This talk will discuss the recent development in cusp catastrophe modeling and its applications to social, behavioral and public health sciences. We will focus on 1) the mathematical and statistical development to provide a mathematic connection between observed data and the deterministic cusp catastrophe at its equilibrium as well as the stochastic cusp catastrophe model for time series data based on Maxwell and Delay conventions, and 2) a novel Bayesian approach combining Hamiltonian Monte Carlo with two likelihood approximation methods, namely Euler approximation and Hermite expansion.
Carlos A. Coelho
NOVA University of Lisbon, Portugal
Carlos A. Coelho is a Full Professor of Statistics at the Mathematics Department of Faculdade de Ciências e Tecnologia of NOVA University of Lisbon. He holds a Ph.D. in Biostatistics by The University of Michigan, Ann Arbor, MI, U.S.A., and his main areas of research are Mathematical Statistics and Distribution Theory, namely the derivation of Likelihood Ratio Tests for elaborate structures of covariance matrices and for MANOVA-like models under the assumption of elaborate covariance structures, as well as the study and development of exact and near-exact distributions for these and other likelihood ratio test statistics used in Multivariate Analysis. Other areas of interest are Estimation, Univariate and Multivariate Linear, Generalized Linear and Mixed Models, etc. Carlos A. Coelho is an Elected Member of the International Statistical Institute and has served as Associate Editor in the Editorial Boards of REVSTAT and Journal of Interdisciplinary Mathematics and currently serves in the Editorial Boards of Journal of Applied Statistics, Journal of Statistical Theory and Practice, American Journal of Mathematical and Management Sciences and Discussiones Mathematicae-Probability and Statistics and is Associate Editor of the Springer Book series “Emerging Topics in Statistics and Biostatistics”. Carlos A. Coelho, a Fulbrighter, is also vice-president of Fulbrighters Portugal – the Portuguese Fulbright Alumni Association.
Speech Title: "On the Distribution of the Product of Independent Beta Random Variables — Applications"
Abstract: The distribution of the product of independent Beta random variables (r.v.’s) is a distribution which plays a key role in Statistics. There are many likelihood ratio test statistics, namely in Multivariate Analysis, whose distribution has been shown to be that of the product of a number of independent Beta r.v.’s. Although along the years many authors have worked on obtaining the distribution of the product of independent Beta r.v.’s in a manageable form or otherwise obtaining manageable approximations for its distribution, obtaining an explicit, accurate, and highly manageable expression for both the probability density function and the cumulative distribution function of this distribution has been a hard task and we are absolutely sure that there is still some room left for improvement. A first approach, based on recently obtained asymptotic expansions of ratios of gamma functions, enables the obtention of the distribution of the product of independent and identically distributed random variables in a much manageable form. However, for the general case, this approach leads to a form which although being very manageable and in line with some previous results, suffers from serious problems of precision and convergence, which have been completely overlooked by other authors and which in most cases prevent its practical use. Nevertheless, it is based on these first results that the authors, using the concept of near-exact distribution, are able to obtain highly manageable but extremely accurate approximations for all cases of the distribution of the product of independent Beta random variables. These near-exact approximations, given their high manageability, accuracy, and proximity to the exact distribution, may in practice be used instead of the exact distribution.