Keynote Speakers

 

ASA Fellow, Prof. Ding-Geng Chen
Arizona State University, USA

Professor (Din) Ding-Geng Chen is an elected fellow of the American Statistical Association (ASA) and an elected member of the International Statistics Institute (ISI). He is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University, USA. He is also the South Africa Research Chair Initiatives (SARChI) Tier-1 research chair in biostatistics established by the Department of Science and Technology (DST), National Research Foundation (NRF), and South Africa Medical Research Council (SAMRC), to lead the research, development and capacity building in biostatistics across Africa, an extraordinary professor at the Department of Statistics, University of Pretoria, South Africa, and an honorary professor at the School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, South Africa. Professor Chen served as a distinguished professor in biostatistics at the University of North Carolina-Chapel Hill, a biostatistics professor at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University, and a professor in biostatistics at the South Dakota State University. His research has been funded by NIH/NSF with more than 200 professional publications and 33 co-authored/co-edited 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: "Integrative Data Harmonization and Statistical Joint Modeling in Evidence-Based Research"

Abstract: Drawing on theory and prior research, evidence-based research can generate data from different sources and phases of research. A central feature of evidence-based research is the sequential and longitudinal implementation with that research participants may drop out or die/censored during the study. This presentation is firstly to give an overview of pitfalls in longitudinal data analysis and further discuss the integrative data harmonization with joint modeling of longitudinal data and time-to-event (such as drop out and censored) data simultaneously using real data from a cardiology multicenter clinical trial (IMPI trial) and HIV/AIDS trial. We demonstrate that the integrative data harmonization has the potential to produce a more efficient and more powerful statistical analysis.

 

Prof. 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: "Is it Possible to Develop a Likelihood Ratio Test for High-Dimensional MANOVA?"

Abstract: The presentation answers the question: “Is it possible to develop a Likelihood Ratio Test (LRT) for the one-way MANOVA problem?”. Although most researchers in the area of Statistics may think that the answer to this question is a ‘No’, we will show that it is indeed a ‘Yes’! The presentation also shows the advantages of the LRT developed in relation to other existing tests, namely in terms of power and control of the Type I error rate. A quite simple, but very well-performing, Normal asymptotic distribution is obtained for the test statistic and a number of simulation results show the advantages of the LRT and its adequacy even for non-normal, highly skewed and/or heavy tailed distributions.