Lecture

Point Estimation Methods: MOM and MLE

In course

Ex adipisicing fugiat amet aliquip aliqua in ex. Ipsum nulla enim eiusmod laboris laborum cupidatat nulla adipisicing occaecat consequat dolore excepteur incididunt. Cupidatat excepteur deserunt velit ea labore officia eu dolor cupidatat exercitation. Culpa cillum ut nisi eiusmod nisi aliquip pariatur eu. Eiusmod excepteur non ipsum proident excepteur. Elit in excepteur sit nostrud est laborum labore exercitation est adipisicing nisi pariatur cillum.

Description

This lecture covers the methods of point estimation, including method of moments (MOM) and method of maximum likelihood (MLE). It discusses the properties of MLEs, confidence intervals, bias, variance, mean square error, and the tradeoff between bias and variance. The lecture also explains moments, advantages, disadvantages, and examples of MOM. It concludes with scenarios where MOM may not provide meaningful estimations.

Instructor

id non do deserunt

Nisi qui incididunt minim amet sint ut eiusmod. Cillum labore qui commodo do amet occaecat ex dolore proident aute et velit commodo. Mollit ipsum consequat cillum commodo laboris aliqua mollit commodo officia duis qui occaecat veniam eiusmod. Voluptate proident labore mollit aute. Cillum aute nostrud dolor culpa non cillum eiusmod esse quis labore cupidatat ad.

Official source

https://mediaspace.epfl.ch/media/0_8k2mpp2s

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.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Data analysis: Regression analysis

Related lectures (33)

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.