SEMINAR & COURSE

Speaker: Barry C. Arnold, Department of Statistics, University of California, Riverside, USA

SEMINAR: 23 September 2026, 14:30

Title: Bivariate models involving independent gamma distributed components

Place: Building I, Samsung room (Auditório Manuel Laranjeira)  

Abstract: 

Several multivariate models involving independent gamma distributed components (three of which are new) are described. The flexible bivariate beta(2) model introduced by Arnold and Ng (2011) provides the template for the other models. It involved ratios of sums of independent gamma variables. The other models involve differences, sums, products and minima rather than ratios.

COURSE: 24 September 2026, 10:00 – 12:00 and 14:00 – 16:00

Title: Beyond multivariate normality. A spectrum of alternative constructions of  bivariate and multivariate distributions

Place: Building VII, room 1.9  

Abstract:

Part I: The classical multivariate normal distribution does constitute a good starting point for our discussion. Our goal is to document various methods for constructing more flexible families of multivariate distributions using well-known (or sometimes, little-known) models as components. We will give many examples of such augmented distributions, but we will not strive to provide an exhaustive listing of all examples that have appeared in the literature. The emphasis will be on the description of methods or algorithms of augmentation, with only a reasonably extensive sample of examples in which the techniques have been or could be used.
Part II: In Chapter 2, we will review positive features of the classical multivariate normal model. However, partially to motivate the study of alternative multivariate models, we will next look carefully at the classical multivariate normal model, paying close attention to the “baggage” that comes along with an assumption of the classical model as an acceptable descriptor of our data. If you accept classical multivariate normality, you must also accept an extensive list of items as being appropriate for your data modeling project.
 

Short Bio:

Barry C. Arnold is Distinguished Professor Emeritus at the Department of Statistics, University of California, Riverside, USA. He received his Ph.D. in Statistics from Stanford University in 1965. He has authored 14 books and more than 300 research papers in reputed peer-reviewed journals and contributed volumes. Professor Arnold has guided 17 Ph.D. scholars and has been on editorial boards of renowned journals. His research interests include estimation theory, probability, stochastic processes, mathematical learning models, biological models, characterizations, income distributions, order statistics, inequality measurement, record values, conditionally specified distributions, and Bayesian inference. He has been invited for scholarly lectures from across the world and is recognized by American Statistical Association, USA (Fellow); American Association for the Advancement of Science, USA (Fellow); Institute of Mathematical Statistics, USA (Fellow); Royal Statistical Society, UK (Fellow); International Statistical Institute, the Netherlands (Elected Member). He also received, in December 2023 an honorary doctorate from the Colegio de Postgraduados, Montecillo, Mexico.