Multivariate Density

Multivariate density estimation aims to model the probability distribution of data with multiple interacting variables, crucial for tasks like clustering, anomaly detection, and predictive modeling. Current research emphasizes robust methods for handling covariate shift and improving accuracy in high-dimensional spaces, employing techniques such as kernel density estimation, spline quasi-interpolation, and low-rank tensor decomposition, often integrated with neural network architectures like GRU-D and Cox-Weibull models. These advancements are impacting diverse fields, from social sciences and epidemiology to reliability analysis and clinical endpoint prediction, enabling more accurate modeling and improved decision-making in complex systems.