Robust High-Dimensional Factor Models with Applications to Statistical Machine Learning

Mathematics

• Statistical Learning 100%
• Factor Models 94%
• Machine Learning 81%
• High-dimensional 60%
• Principal Component Analysis 52%
• Random Projection 35%
• Matrix Perturbation 35%
• Robust Statistics 34%
• Low-rank Matrices 32%
• Network Analysis 32%
• Neuroscience 32%
• False Discovery Rate 32%
• Multiple Testing 30%
• Statistical Estimation 30%
• Genomics 29%
• Factor Analysis 28%
• Matrix Theory 28%
• Finance 25%
• Perturbation Theory 25%
• Dimensionality 25%
• Model Selection 24%
• Statistical Model 24%
• Recovery 24%
• Statistical Inference 23%
• Economics 21%
• Statistics 17%
• Dependent 16%
• Class 7%

Business & Economics

• Machine Learning 97%
• Principal Component Analysis 67%
• Matrix 43%
• Perturbation Theory 40%
• Robust Statistics 40%
• Model Selection 37%
• Statistical Learning 35%
• Statistical Estimation 35%
• Statistical Inference 34%
• Network Analysis 34%
• Statistical Model 34%
• Factor Analysis 31%
• Neuroscience 29%
• Toolkit 28%
• Dimensionality 27%
• Finance 24%
• Testing 20%
• Statistics 18%
• Factors 17%
• Economics 12%