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多元Lévy自适应B样条回归AI 解读

We propose Multivariate Lévy Adaptive B-Spline Regression (MLABS), a Bayesian nonparametric regression model designed to capture nonlinear effects, interactions, and locally varying features of multivariate functions. MLABS represents the regression function as a random sum of low-order tensor-product B-spline basis functions under a finite Lévy random measure prior. The resulting representation adapts to the complexity and local structure of the regression function while…

高维近似因子模型中特征结构的贝叶斯估计AI 解读

High-dimensional economic datasets often display strong co-movement driven by a small number of latent factors, which are typically modeled using approximate factor models. When the number of variables is large relative to the sample size, the eigenvalues and eigenvectors of the sample covariance matrix are severely distorted, which in turn makes principal component based estimators of the factor structure unstable. To address the high-dimensional problem, we propose a…

高维协方差特征结构的广义收缩逆Wishart先验推断AI 解读

In multivariate statistics, estimating the covariance matrix is essential for understanding the interdependence among variables. In high-dimensional settings, where the number of covariates increases with the sample size, it is well known that the eigenstructure of the sample covariance matrix is inconsistent. The inverse-Wishart prior, a standard choice for covariance estimation in Bayesian inference, also suffers from posterior inconsistency. To address the issue of…