Nonparametric statistics: Nonparametric regression and smoothing · Introduction

Introduction

What this guide covers

A comprehensive and mathematically rigorous treatment of functional estimation, kernel smoothing, local polynomials, penalized splines, and additive models, fundamental to modern distribution-free statistical inference.

Overview

Topics covered

Introduction
Non-Parametric Regression and Smoothing Methods
Theoretical Framework of Non-Parametric Regression
The Fundamental Dilemma: Bias-Variance Tradeoff
Kernel-Based Estimators (Kernel Smoothing)
The Nadaraya-Watson Estimator

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

m(x) = \mathbb{E}[Y \mid X = x] = \int_{\mathbb{R}} y \, f_{Y|X}(y|x) \, dy

View LaTeX source

m(x) = \mathbb{E}[Y \mid X = x] = \int_{\mathbb{R}} y \, f_{Y|X}(y|x) \, dy