A0491
Title: Modeling heterogeneity in higher-order moments while preserving mean and variance: Application to spatiotemporal data
Authors: Hajime Kuno - The Graduate University for Advanced Studies (Japan) [presenting]
Abstract: The aim is to propose a general model capable of addressing heterogeneity in higher-order moments while preserving mean and variance, including the t, Laplace, and skew-normal distributions as special cases. The model flexibly accommodates variations in tail heaviness and asymmetry at each data point while maintaining interpretability similar to normal distribution models. Notably, it is closed under linear transformations and provides explicit analytical expressions for skewness and kurtosis. The proposed model is applied to spatial and temporal data analysis, demonstrating that its properties vary based on the chosen matrix decomposition approach. To facilitate efficient inference, a Bayesian estimation method is developed using data augmentation, which is particularly effective for temporal models. Simulation studies confirm that accounting for heterogeneity in higher-order moments enhances parameter estimation accuracy and predictive performance. To illustrate real-world applicability, production functions are analyzed across U.S. states. The results indicate that the model effectively captures heterogeneity in higher-order moments, leading to superior model fit in empirical data analysis.
