Nonparametric statistics: Foundations of nonparametric inference · Introduction

Introduction

What this guide covers

A rigorous approach to empirical measurement concentration theory, statistical functionals in Banach spaces, distribution-free hypothesis testing and Kernel smoothing methods.

Overview

Topics covered

Introduction
Non-Parametric Statistical Inference Fundamentals and Asymptotic Theory
1. Paradigms and Efficiency of Non-Parametric Inference
Rigorous Comparison
Asymptotic Relative Efficiency
2. The Empirical Distribution Function (EFD) and its Concentration Analysis

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

\mathcal{P} = \left\{ P_\theta : \theta \in \Theta \subseteq \mathbb{R}^d \right\}

View LaTeX source

\mathcal{P} = \left\{ P_\theta : \theta \in \Theta \subseteq \mathbb{R}^d \right\}