Cognitive Science Program

Dr. Tara McAllister, Research Fellow, Victoria University of Wellington, Wellington, New Zealand

Host: Dr. Kat Milligan-McClellan

“Formidable Phormidium: toxic cyanobacteria in New Zealand rivers”

Summary: In this seminar, Dr McAllister will explore the physicochemical factors that lead to toxic benthic cyanobacterial blooms in New Zealand rivers. She will present the findings of two experiments that collectively aimed to understand the effects of nutrients and velocity on Phormidium growth

Bio: Dr Tara McAllister (Te Aitanga a Māhaki) is an Indigenous scientist, interdisciplinary scholar and a mother. She is currently a Research Fellow at the School for Science in Society at Te Herenga Waka — Victoria University of Wellington in New Zealand. She holds a PhD in Water Resource Management from the University of Canterbury and has expertise spanning freshwater ecology and racism in higher education.

Publication: The rise of toxic benthic Phormidium proliferations: a review of their taxonomy, distribution, toxin content and factors regulating prevelance and increased severity.

SHaW is hosting Pet Therapy every Tuesday from 5pm - 7pm at the Cordial Storrs House.
Stop by for some pet therapy love!

We’ve also partnered with the The Benton to host Pet Therapy every first Thursday of the month!
Join us in The Benton from 4:30 – 6:30 p.m.

Please note that Pet Therapy is provided on a volunteer basis. We cannot guarantee that dogs will be present the entire time.

Dr. Flora Oswald, University of Connecticut

Social perceptions and experiences among marginalized group members: Implications for intergroup disparities

Professor Sujit K. Ghosh, Ph.D.
Department of Statistics
North Carolina State University

Recipient of the 2023 UConn Statistics Department
Distinguished Alumnus Award

Nonparametric Estimation of Multivariate Copula using Empirical Bayes Method

In the field of finance, insurance, and system reliability, etc., it is often of interest to measure the dependence among variables by modeling a multivariate distribution using a copula. The copula models with parametric assumptions are easy to estimate but can be highly biased when such assumptions are false, while the empirical copulas are non-smooth and often not genuine copula, making the inference about dependence challenging in practice. As a compromise, the empirical Bernstein copula provides a smooth estimator, but the estimation of tuning parameters remains elusive. In this paper, by using the so-called empirical checkerboard copula, we build a hierarchical empirical Bayes model that enables the estimation of a smooth copula function for arbitrary dimensions. The proposed estimator based on the multivariate Bernstein polynomials is itself a genuine copula, and the selection of its dimension-varying degrees is data-dependent. We also show that the proposed copula estimator provides a more accurate estimate of several multivariate dependence measures, which can be obtained in closed form. We investigate the asymptotic and finite-sample performance of the proposed estimator and compare it with some nonparametric estimators through simulation studies. An application to portfolio risk management is presented, along with a quantification of estimation uncertainty. [This presentation is based on a joint work with my former student Dr. Lu Lu]

DATE: Wednesday, September 27, 2023
TIME: 4:00 pm
PLACE: AUST 110
WebEx: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m44f5da414545274b83c84dec11d638d5

Coffee will be served 3:30-4:00 in the Noether Lounge (AUST 326)