Simulation: Resampling and Advanced Bootstrap Methods

Introduction

What this guide covers

Non-parametric inference, mathematical quantification of error, estimation of biases, variances and confidence intervals through probabilistic resampling.

Overview

Topics covered

• Introduction
• Fundamentals and Non-Parametric Inference
• 1.1 Introduction and Philosophy of Resampling
• 1.2 The Substitution Principle (Plug-in Principle)
• 1.3 The Empirical Distribution Function (ECDF)
• The Non-Parametric Bootstrap

Related concepts

Main connections

• Bootstrap resampling
• Variance & standard deviation
• Calculus & differentiation
• Mean & expected value
• Quantiles & percentiles
• Bayes' theorem & priors

Compatible notation

Mathematical sample in LaTeX

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