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Advanced & Applied Topics · High-Dimensional Statistics & Sparsity

FDR and dimensionality reduction in high dimensions

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Chart from the original guide

Overview

Topics covered

Introduction
FDR and High Dimensional Inference
The problem of high dimension and the curse of dimensionality
1. Concentration of the measurement in high-dimensional spaces
2. Spurious correlations and noise accumulation
3. The Sparsity Paradigm

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

V_p(R) = \frac{\pi^{p/2}}{\Gamma(p/2 + 1)} R^p

View LaTeX source

V_p(R) = \frac{\pi^{p/2}}{\Gamma(p/2 + 1)} R^p