Lecture

Non-Parametric Estimation: Kernel Density Estimators

Description

This lecture delves into non-parametric estimation techniques, focusing on kernel density estimators to estimate distribution functions and parameters. The instructor explains how to estimate mean, variance, and median using non-parametric methods, highlighting the importance of bandwidth selection in minimizing integrated mean squared error. The lecture also covers the plug-in principle, the Dirac comb density function, and the trade-off between bias and variance in estimation. Additionally, it discusses the challenges of choosing an optimal bandwidth and the implications of different kernel choices on the accuracy of density estimates.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.