χ2-Statistics for High-Dimensional Inference of Multivariate Generalized Linear Models

Explore advanced statistical methods for analyzing high-dimensional multivariate generalized linear models through chi-squared statistics in this 53-minute conference talk from ICBS2025. Delve into the theoretical foundations and practical applications of χ2-statistics as they apply to complex, high-dimensional datasets where traditional inference methods may fall short. Learn how these statistical techniques can be effectively employed to make robust inferences about multivariate relationships in scenarios where the number of variables approaches or exceeds the sample size. Gain insights into the mathematical framework underlying these methods and understand their significance in modern statistical analysis, particularly in fields dealing with large-scale data such as genomics, finance, and machine learning. Discover the challenges and solutions associated with high-dimensional inference and how chi-squared statistics provide a powerful tool for navigating the complexities of multivariate generalized linear modeling in contemporary data science applications.