.png){kind=icon}
返回
Robust High-Dimensional Factor Models with Applications to Statistical Machine Learning
.png){kind=icon}
OA
AI
DOI：10.1214/20-STS785.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSelect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and provide powerful tools for statistical estimation and inference. We highlight PCA and its connections to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery.
Keyword:
Factor model
PCA
covariance estimation
perturbation bounds
robustness
random sketch
FarmSelect
FarmTest
期刊
IF:
3.4
论文数:
1.0K
被引数:
8.7K
机构
引用论文
Recognition Acuity, Grating Acuity, Contrast Sensitivity, and Visual Fields in 6-Year-Old Children
PREAI
分享收藏Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptionsOAAI
OAAI分享收藏Preconditioning for feature selection and regression in high-dimensional problems'.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏THE TWO-TO-INFINITY NORM AND SINGULAR SUBSPACE GEOMETRY WITH APPLICATIONS TO HIGH-DIMENSIONAL STATISTICS二到无穷范数和奇异子空间几何及其在高维统计中的应用ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏分享收藏
查看更多内容