IDWSDS 2025 - S110: Embracing Complexity: Advanced Statistical and Computational Methods in Modern.. скачать в хорошем качестве

IDWSDS 2025 - S110: Embracing Complexity: Advanced Statistical and Computational Methods in Modern..

Embracing Complexity: Advanced Statistical and Computational Methods in Modern Data Analysis This session showcases cutting-edge statistical and machine learning research that tackles complex data challenges using sophisticated computational and analysis techniques. While the first three presentations span innovative Bayesian methodologies, including nonparametric hypothesis testing for multiple comparisons, adaptive LASSO quantile regression for handling missing data, and dynamic spatiotemporal modeling for real-time forecasting, the last one introduces a state-of-the-art application of deep learning and natural language processing in e-commerce fake review detection. Together, these talks highlight the ongoingevolution of modern statistics and data analysis practices, emphasizing thedevelopment and utilization of these advanced methods to extract meaningfulinsights from increasingly intricate datasets across diverse domains. ORGANIZER AND CHAIR: Mai Dao, Wichita State University TITLES AND SPEAKERS: Bayesian Nonparametric Hypothesis Testing Methods on Multiple Comparisons, Zhuanzhuan Ma, The University of Texas Rio Grande Valley Bayesian Adaptive LASSO Quantile Regression with Non- Ignorable Missing Responses, Mai Dao, Wichita State University Dynamic Bayesian Spatiotemporal Modeling for Real-Time Disease Forecasting with Likelihood-Based Weighting, Nadeesha Jayaweera, The University of Akron Detecting Fake Reviews on Amazon Using Deep Learning and NLP Techniques, Thilini Jayasinghe, University of Dayton