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Shan、Ying 与 Zhao 对《作为线性回归的增广平衡权重》的讨论AI 解读

Jiawei Shan, Chao Ying, Jiwei Zhao; Shan, Ying and Zhao’s contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smi

内蕴黎曼函数型充分降维及其扩展AI 解读

This article focuses on linear sufficient dimension reduction with Riemannian random processes as predictors and complex random objects in a metric space as responses. We propose two novel methods—Intrinsic Riemannian Functional Weighted Inverse Regression Ensemble (iRF-WIRE) and Intrinsic Riemannian Functional Weighted Directional Regression (iRF-WDR)—to recover the central subspace. These methods can be readily extended to Wasserstein functional predictors. We establish…

随机对象的 Fréchet 充分降维AI 解读

Summary We consider Fréchet sufficient dimension reduction with responses being complex random objects in a metric space and high-dimensional Euclidean predictors. We propose a novel approach, called the weighted inverse regression ensemble method, for linear Fréchet sufficient dimension reduction. The method is further generalized as a new operator defined on reproducing kernel Hilbert spaces for nonlinear Fréchet sufficient dimension reduction. We provide theoretical…

因子成分与特异成分的经验过程AI 解读

The distributions of the common and idiosyncratic components for an individual variable are important in forecasting and applications. However, they are not identified with low-dimensional observations. Using the recently developed theory for large dimensional approximate factor model for large panel data, the common and idiosyncratic components can be estimated consistently. Based on the estimated common and idiosyncratic components, we construct the empirical processes for…