Virtual Seminar on April 16, 2025 at 1 PM CST
Title: Enhancing Bankruptcy Prediction: A Two-Layered Network Approach Using Latent Space Models
Speaker: Dr. Tianhai Zu
`Department of Management Science and Statistics at the Carlos Alvarez College of Business
UTSA
Abstract:
In this study, we present a novel statistical approach to corporate bankruptcy prediction by leveraging complex network analysis. We introduce a two-layered network structure that captures both supply chain relationships and investment-co-investment patterns among companies, providing a more comprehensive view of corporate interdependencies than traditional methods. To analyze this complex structure, we develop a flexible multi-layered latent position model that efficiently extracts key features from the network. Our methodology employs advanced statistical techniques to estimate latent positions underlying this two- layered network, which are then utilized as predictors in a bankruptcy prediction model. Using the US public company data, we demonstrate that incorporating these network-derived features significantly enhances the predictive power of bankruptcy models. Our results reveal that these latent positions estimated from network structure capture crucial relational information that is highly relevant to a company's financial stability. This approach not only outperforms traditional prediction methods but also provides interpretable insights into the role of corporate interconnectedness in financial risk. Our work aims to offer a robust statistical framework for integrating complex relational data into predictive modeling for bankruptcy risk assessment.
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Virtual Seminar on March 26, 2025 at Noon
Speaker
Suprateek Kundu, PhD
Department of Biostatistics
The University of Texas at MD Anderson Cancer Center
Associate Editor, Biometrics
Elected Member, International Statistical Institute
Title
Automated Learning of Heterogeneity via Global-Local Clustering for High-Dimensional Data
Abstract
Uncovering hidden heterogeneity in high-dimensional data is a fundamental challenge in modern data science, with applications in time-series modeling, neuroimaging, and spatial transcriptomics. Traditional Bayesian nonparametric clustering methods, particularly Dirichlet process (DP) mixtures, have been widely successful in borrowing information across samples. However, DP-induced global clustering patterns often fail in complex heterogeneous settings where clustering structures vary across scales, feature subsets, or spatial regions. While some local clustering methods address increased heterogeneity, they often lack scalability for high-dimensional functions, and their theoretical properties remain underexplored.
We overcome these limitations by introducing a novel class of product Dirichlet process location-scale mixtures that enable independent clustering at multiple scales. The proposed approach first identifies mutually exclusive partitions of the data and then clusters each partition separately using independent DP priors. This results in a scalable global-local clustering framework, where elements within a partition share identical atoms (global clustering), while distinct partitions are clustered independently (local clustering). We develop efficient MCMC algorithms for implementation and establish asymptotic posterior consistency properties.
Our contributions span two studies: the first focuses on clustering high-dimensional subject-specific parameters in vector autoregressive (VAR) models for functional neuroimaging applications, while the second tackles the clustering of high-dimensional spatial functions, motivated by spatial transcriptomics in breast cancer. Extensive simulations demonstrate improved clustering and estimation compared to classical global and local approaches that suffer from the curse of dimensionality. Our methods make a significant contribution to the high-dimensional clustering literature by bridging the gap between global and local clustering approaches.
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2025 Conference of Texas Statisticians
at
Sam Houston State University
The Woodlands Center, 3380 College Park Dr, TX 77384
We are excited to announce that abstract submissions for contributed oral presentations, student poster submissions, and registration are now open for the 44th Annual Conference of Texas Statisticians (COTS), March 28-29, 2025. The conference will take place at the Sam Houston State University, The Woodlands Center, located at 3380 College Park Dr, The Woodlands Center, TX 77384. The theme of the conference is “Data Science, AI, Machine Learning and Other Related Statistical Techniques with Applications”. We look forward to your presence at this insightful gathering.
To participate, please register and submit your abstract and posters by the deadline of February 26th, 2025. For detailed information regarding the registration, please visit the conference website at (University website?) . We look forward to your active involvement in this exciting event.
For more information contact:
Ferry Butar Butar, Ph.D.
butar@shsu.edu
https://sites.google.com/view/cots-2025ferrybutarshsu/
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Virtual Lecture-November 22, Friday 11 AM- 12 Noon, CST
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Title: The Bayesian Optimal Interval Design (BOIN) A Review of Theory and Methods with Application to a Phase I trial at UT Health San Antonio
Speaker: Joel Michalek PhD FASA, UTSAHSC
The Bayesian Optimal Interval Design for a single drug is reviewed and applied to a Phase I trial at the Mays Cancer Center, UT Health San Antonio. The design, parameterization, optimality, and methods are reviewed. A real-world application to a recently approved Phase I trial of AXL and JAK inhibitors in patients with adenocarcinoma of the lung is explained.
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Virtual Lecture-October 25, Friday 2 PM- 3PM, CST
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Exascale Geostatistics for Environmental Data Science
Speaker: Dr. Marc G. Genton, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Environmental data science relies on some fundamental problems such as: 1) Spatial Gaussian likelihood inference; 2) Spatial kriging; 3) Gaussian random field simulations; 4) Multivariate Gaussian probabilities; and 5) Robust inference for spatial data. These problems develop into very challenging tasks when the number of spatial locations grows large. Moreover, they are the cornerstone of more sophisticated procedures involving non-Gaussian distributions, multivariate random fields, or space-time processes. Parallel computing becomes necessary for avoiding computational and memory restrictions associated with large-scale environmental data science applications. In this talk, I will explain how high-performance computing can provide solutions to the aforementioned problems using tile-based linear algebra, tile low-rank approximations, as well as multi- and mixed-precision computational statistics. I will introduce ExaGeoStat, and its R version ExaGeoStatR, a powerful software that can perform exascale (10^18 flops/s) geostatistics by exploiting the power of existing parallel computing hardware systems, such as shared-memory, possibly equipped with GPUs, and distributed-memory systems, i.e., supercomputers. I will then describe how ExaGeoStat can be used to design competitions on spatial statistics for large datasets and to benchmark new methods developed by statisticians and data scientists for large-scale environmental data science.
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Treasurer's Report 6-24-2024
The chapter is receiving roughly $320 in dues annually. We have not had a traveling course since 2018 so the expenses are limited to the Don Owen Award for which $500 was paid to David Allison in 2020 and $500 was paid to Stefan Steiner in 2021. Since the 2020 & 2021 COTS were virtual, no travel expenses were involved. Since the 2024 COTS was in person, Marc Genton was reimbursed for $500 in travel expenses and $150 for registration in addition to the $500 cash award.
COTS-Student and Junior Faculty Travel Award
Key dates: April 22, 2024
COTS, ASA-San Antonio Chapter, and NSF are pleased to provide limited travel, lodging, and registration awards to PhD students and junior faculty who participate in invited or contributed program in COTS 2024. The applications for travel awards are due by April 22, 2024.
Eligibility: A. Student Award: The student must be enrolled in a degree-granting institution on May 1, 2024. The student must be the first author of the submitted paper/poster. The student must be willing to attend and present the paper/poster in person at the COTS 2024.
B. Junior Faculty Award: The junior faculty must be a faculty member at an institution and must be within 5 years of receiving their Ph.D. degree on May 1, 2024. The junior faculty must be the first author of the submitted paper. The junior faculty must be willing to attend and present the paper in person at the COTS 2024.
Applications: Please use the link below to submit your application by April 22, 2024. This application form will ask students to upload a letter from their PhD supervisor that certifies their PhD student status. It will also ask students to upload the abstract as a PDF file, with which students are strongly encouraged to submit their paper as a PDF file to strengthen their application. The paper is limited to 30 pages double-spaced.
https://forms.gle/VMksbbPpqdKCqt4W8
Conference of Texas Statisticians
May 9-10, 2024
We are excited to announce that abstract submission and registration are now open for the 43rd Annual Conference of Texas Statisticians (COTS). The conference is scheduled to take place on May 9- May 10, 2024, in Houston, Texas, with a focus on the theme "AI, Machine Learning, and Other Related Statistical Techniques with Applications." The conference will take place at Houston Methodist Research Institute, 6670 Bertner Ave, Houston, TX 77030.
The Conference of Texas Statisticians (COTS) originated at the annual American Statistical Association (ASA) meeting in Houston in 1980, with its inaugural session held in Waco in 1981. Since 1985, the Texas Chapters of the ASA have been integral in establishing the Council of Texas Statisticians, featuring representatives from each chapter. This conference serves as a platform for statisticians to engage in both social and intellectual exchanges with fellow researchers. COTS welcomes researchers at all levels—senior and junior, as well as students—to present their research talks or posters at COTS, fostering a rich and collaborative environment.
To participate, please register and submit your abstract by the deadline of April 5th, 2024. For detailed information regarding abstract submission, registration, and important dates, please visit the conference website at https://learn.houstonmethodist.org/AI-2024. We look forward to your active involvement in this exciting event.
The scientific program for the conference will encompass invited sessions, oral contributed sessions, and posters. More details will be shared in the upcoming communications.
As for the social program, poster session and festivities will commence on the evening of Thursday, May 9th, with the Researchers’ Gathering. This event will serve as a platform for researchers to connect, network, and foster a sense of camaraderie at the conference.
The Welcome Reception and opening ceremony are scheduled for Friday morning, May 10th. Delectable lunch and coffee will be provided during the conference on May 10th, 2024.
The pinnacle of the social events will be the Conference Dinner and the presentation of the prestigious Don Owen Award and other awards on the evening of Friday, May 10th. Join us for a memorable and enriching experience.
Discover Houston, the fourth-largest city in America, boasting a cosmopolitan charm with world-class dining, arts, hotels, shopping, and nightlife. Whether wandering through the historic Heights, exploring the Museum District, or visiting Space Center Houston, the city offers a diverse array of experiences. Houston's vitality and cultural richness, coupled with its dynamic blend of imagination, talent, and premier attractions, truly position it as a world-class city. With a thriving economy, stunning surroundings, and a population radiating optimism and spirit, Houston stands as a sought-after international destination.
We look forward to seeing you at the conference.
Thanks,
Sunil
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Sunil Mathur, Ph.D.
Director of Biostatistics and Professor of Biostatistics in Medicine at Weill Cornell Medical College
Co-Director, Biostatistics and Biomedical Informatics Shared Resource
Editor-in-Chief: American Journal of Statistical Science and Applications
Elected Member of the International Statistical Institute
President-American Statistical Association-San Antonio Chapter
Organizer-Conference of Texas Statisticians-2024
Houston Methodist Neal Cancer Center
Houston Methodist Research Institute
6565 Fannin St, Houston, TX 77030
Tel: 713-363-9033
Email: smathur2@houstonmethodist.org