Statistical inference on various data structure
Conference
Category: International Statistical Institute
Proposal Description
Modern statistical methodologies must account for complex data structures that arise in various fields, including finance, biology, and social sciences. This session explores innovative techniques including random effect models, Brownian motion, and topological data analysis to enhance statistical inference and data representation. Using these methodologies, researchers can better capture dependencies, model stochastic behavior, and extract meaningful topological features from high-dimensional datasets. The session will feature leading researchers discussing theoretical advancements, computational implementations, and real-world applications.