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Contributed Papers

Computing Advances in Sampling Methodology & Categorical Data

Sun, Aug 3, 2:00 PM - 3:50 PM Room CC-103B Music City Center

Li-Hsiang LinChair

Section on Statistical Computing

7 Presentations

Advances in Exact Subsampling Methods with Linear Regression Models

2:05 PM - 2:20 PM

Co-authors: Jiayi Zheng, Jiayi Zheng, Nicholas Rios (George Mason University)

Experiment Design Big Data Subsampling I-optimality

The A Priori Procedure for estimating the location parameter in the Unified Skew Normal distribution

2:20 PM - 2:35 PM

Co-authors: Cong Wang (University of Nebraska at Omaha), Weizhong Tian, Cong Wang (University of Nebraska at Omaha)

A priori procedure Unified skew-normal distribution Sample size Confidence interval Coverage probability

Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions

2:35 PM - 2:50 PM

Co-authors: Marc Brooks, Gabriel Durham (University of Michigan), Marc Brooks, Kihyuk Hong (University of Michigan)

Thompson Sampling Contextual Bandit Just-in-Time Adaptive Interventions Mobile Health (mHealth)Reinforcement Learning Large Language Models (LLMs)

Comparing Entropy Balancing & IPTW for Cohort Balancing: Case Studies from Real-World Claims Data

2:50 PM - 3:05 PM

Co-authors: Ramaa Nathan (EVERSANA), Ramaa Nathan (EVERSANA), Jason Poh (EVERSANA), Mostafa Shokoohi (EVERSANA)

Entropy Balancing,Inverse Probability Treatment Weighting (IPTW)Real World Data Observational studies Multinomial Second moments

AI-powered joint model of longitudinal and survival outcomes with various survival loss functions

3:05 PM - 3:20 PM

Co-authors: Sori Lundin (The University of Texas Science Center At Houston), Ruosha Li (University of Texas School of Public Health), Sori Lundin (The University of Texas Science Center At Houston), Wenyaw Chan (University of Texas-Houston), Xi Luo (University of Texas Health Science Center At Houston), Cui Tao (Mayo Clinic Department of Artificial Intelligence and Informatics)

AI Joint modeling Neural Networks Prognosis Prediction

Effect of Training Data Quality on Classifier Performance

3:20 PM - 3:35 PM

Co-authors: Jeanne Ruane (University of Pennsylvania), Alan Karr (Temple University), Alan Karr (Temple University)

Classifier Training data Data quality Phase transition

On finite mixture modeling and model-based clustering of multivariate categorical sequences

3:35 PM - 3:50 PM

Co-authors: Yingying Zhang (Western Michigan University), Volodymyr Melnykov (University of Alabama), Yingying Zhang (Western Michigan University)

EM algorithm Finite mixture model Markov model model-based clustering multivariate categorical sequences

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