FDR, statistical learning, and classification in high dimensions

Introduction

What this guide covers

Introduction Multiple Hypotheses & Regularization

Overview

Topics covered

• Introduction
• False Discovery Rate (FDR) and High-Dimensional Classification
• The Challenge of High Dimension and Multiple Tests
• 1.1 Paradigmatic example: Dead salmon in fMRI
• The Formal Mathematical Framework of Multiple Hypothesis Testing
• Classic FWER Control: Disadvantages and Limits in High Dimension

Related concepts

Main connections

• Multiple testing & FWER/FDR
• Regularization (Lasso / Ridge / Elastic Net)
• p-value & significance
• Supervised machine learning
• Hypothesis testing
• Linear algebra & matrices

Compatible notation

Mathematical sample in LaTeX

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