ASA Connect

Dear Colleagues, 

The ASA Statistical Learning and Data Science Section is pleased to announce its August webinar, presented by Professor Jiashun Jin on August 24, 2023.

Title:                         The Statistics Triangle

Speakers:                Dr. Jiashun Jin, Department of Statistics & Data Science, Carnegie Mellon University

Date and Time:       August 24, 2022, 4:00 to 5:30 pm Eastern Time

Registration Link:   ASA SLDS Webinar Registration Link [eventbrite.com]

Abstract:                 In his Fisher's Lecture in 1996, Efron suggested that there is a philosophical triangle in statistics with "Bayesian", "Fisherian", and "Frequentist" being the three vertices, and many representative statistical methods can be viewed as a convex linear combination of the three philosophies. We collected and cleaned a data set consisting of the citation and bibtex (e.g., title, abstract, author information) data of 83,331 papers published in 36 journals in statistics and related fields, spanning 41 years. Using the data set, we constructed 21 co-citation networks, each for a time window between 1990 and 2015. We propose a dynamic Degree-Corrected Mixed- Membership (dynamic-DCMM) model, where we model the research interests of an author by a low-dimensional weight vector (called the network memberships) that evolves slowly over time. We propose dynamic-SCORE as a new approach to estimating the memberships. We discover a triangle in the spectral domain which we call the Statistical Triangle, and use it to visualize the research trajectories of individual authors. We interpret the three vertices of the triangle as the three primary research areas in statistics: "Bayes", "Biostatistics" and "Nonparametrics". The Statistical Triangle further splits into 15 sub-regions, which we interpret as the 15 representative sub-areas in statistics. These results provide useful insights over the research trend and behavior of statisticians.

Presenter:               Jiashun Jin is Professor in Statistics & Data Science and Affiliated Professor in Machine Learning at Carnegie Mellon University. He received his Ph.D in Statistics from Stanford University in 2003. His earlier work was on large-scale multiple testing, focusing on the development of (Tukey's) Higher Criticism and practical False Discovery Rate (FDR) controlling methods. His more recent interest is on the analysis of social networks and text documents, focusing on the development of the SCORE normalization and on cycle count methods. He has also led a team collecting and analyzing a high-quality large-scale data set on the publications of statisticians.

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Zhihua Su, PhD
Associate Professor
Department of Statistics
University of Florida
zhihuasu@stat.ufl.edu
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