Some New Developments On Pairwise Likelihood Estimation & Testing In Latent Variable Models Presented by DR. IRINI MOUSTAKI, LONDON SCHOOL OF ECONOMICS Friday, December 1 11:00 a.m. ET Virtual meeting – Webex meeting link Pairwise likelihood is a limited-information method used to estimate latent variable models, including factor analyses of categorical data. It avoids evaluating […]
Events
Joint Collaborative Statistics Colloquium: Jun Yan, University of Connecticut
Joint Collaborative Statistics Colloquium Network analytics with applications to input-output tables Presented by Professor Jun Yan, Department of Statistics, University of Connecticut Thursday, Dec. 7 2023 8:00 PM-9:00 PM ET (Friday, Dec. 8, 9 AM Beijing) AUST 110 Webex Meeting Link Voov Meeting Link (meeting id: 861-447-545) Weighted, directed networks defined by national or multi-regional […]
Becoming Career Ready: Skills Employers Seek
On November 30th from 5PM – 6PM, join staff from the Center for Career Development, a Statistics Alum, and a University Recruiter for Statistics, Mathematics-Statistics, and Statistical Data Science majors as they discuss how the NACE Career Competencies can be influential in the internship/job application process. Learn more about what the NACE Career Competencies are, why […]
UConn/UMass Joint Colloquium: Dipak Dey, University of Connecticut
Generalized Variable Selection Algorithms for Gaussian Process Models Presented by Dipak Dey University of Connecticut Webpage: http://merlot.stat.uconn.edu/~dey/ Wednesday, Nov 8 2023 4:00 PM-5:00 PM ET UMass LGRT 1681 Zoom Meeting Link With the rapid development of modern technology, massive amounts of data with complex pattern are generated. Gaussian process models that can easily fit the […]
Statistics Colloquium: Davide Viviano, Harvard University
Policy Targeting under Network Interference Econometrics-Statistics Joint Seminar Presented by Davide Viviano , PhD Harvard University Host: Jungbin Hwang/Yao Zheng Friday, Oct 27 2023 1:25 PM-2:45 PM ET OAK 337 Zoom Meeting Link This paper studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method […]
Statistics Colloquium: Norou Diawara, Old Dominion University
Copula Based Models for Bivariate Zero-Inflated Count Time Series Data Presented by Norou Diawara, PhD Professor of Statistics, Old Dominion University Wednesday, Nov 1 2023 4:00 PM-5:00 PM ET AUST 110 Webex Meeting Link Count time series data have multiple applications. The applications can be found in areas of climate, public health, crime data analyses […]
Statistics Colloquium: Sanjib Basu, University of Illinois Chicago
Bayesian Nonparametric Modeling of Restricted Mean Survival Time Presented by Sanjib Basu, PhD Distinguished Professor, University of Illinois Chicago Director, Center for Biostatistical Development Wednesday, Oct 18 2023 4:00 PM-5:00 PM ET AUST 110 Webex Meeting Link Restricted mean survival time (RMST) is increasingly being used in planning and analyzing time-to-event outcome in clinical, medical […]
Interdisciplinary Seminar: Dr. Xinyuan Song, The Chinese University of Hong Kong
Hidden Markov Models With An Unknown Number Of Hidden States Presented by Dr. Xinyuan Song, The Chinese University of Hong Kong Friday, NOVEMBER 3 10:00 a.m. ET Virtual meeting – Webex meeting room Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically […]
Interdisciplinary Seminar: Wes Bonifay, Stanford University
Uncovering the Hidden Complexity of Statistical Models Presented by Dr. Wes Bonifay, University of Missouri Friday, October 13 11:00 a.m. ET Virtual meeting – Webex meeting room Model complexity is the ability of a statistical model to fit a wide range of data patterns. Complexity is routinely assessed by simply counting the number of freely […]
Paper of the Month: October 2023
Statistical Hypothesis from the view point of Fisher, Jeffreys and Neyman: Agreements and Disagreements