每日简报

国际溢出的结构矩阵自回归框架AI 解读

Understanding how macroeconomic shocks propagate across countries requires structural models that can jointly identify country-specific shocks and their international transmission. Yet extending structural vector autoregressions (SVARs) to large multi-country systems is challenging due to rapidly increasing dimensionality, computational costs, and the proliferation of identifying restrictions. This paper develops a Bayesian Structural Matrix Autoregression (BSMAR) framework…

随机梯度下降：非线性时间序列视角AI 解读

This paper investigates the statistical behavior of Stochastic Gradient Descent (SGD) with constant step size under the framework of iterated random functions. Compared to previous studies establishing the convergence of SGD in probability measure, e.g., Wasserstein distance, our approach directly provides the convergence in Euclidean distance by showing the Geometric Moment Contraction (GMC) of SGD. This new convergence can address the non-stationarity of SGD due to fixed…

因子辅助密度估计：当环境维度带来神谕速率AI 解读

We study density estimation in the high-dimensional factor model, whose fixed-dimensional latent factors have an unknown smooth density. We propose factor-assisted density estimation (FADE), a non-iterative procedure that applies kernel density estimation to spectral estimates of the latent scores. The score proxies are dependent and do not follow the latent density, so standard kernel-density arguments do not apply. A refined rowwise expansion separates error into a…

面向微生物组计数的贝叶斯非参数纵向因子模型AI 解读

Longitudinal microbiome studies provide important insights into the temporal evolution of microbial abundances and dynamic cross-feature dependence. Modeling longitudinal microbiome data is challenging because microbiome counts are highly heterogeneous and zero-inflated, and, moreover, repeated measurements induce within-subject temporal dependence and possibly time-varying cross-feature covariance. To address these challenges, we develop a flexible Bayesian longitudinal…

conformalForecast：时间序列序贯共形预测的 R 包AI 解读

Reliable uncertainty quantification is central to modern forecasting. Conformal prediction offers model-agnostic intervals with coverage guarantee under exchangeability, but existing software often targets general prediction problems or individual time series procedures rather than integrated workflows for multistep forecasting applications. We introduce conformalForecast, an open-source R package for constructing, updating, and evaluating conformal prediction intervals for…