# method of moments

(1.4 hours to learn)

## Summary

The method of moments is a simple method for estimating the parameters of a probability distribution from data. The parameters are chosen so that the model moments match the empirical moments.

## Context

This concept has the prerequisites:

- expectation and variance (The method of moments requires estimating moments.)

## Goals

- Understand what the method of moments estimator is and how to compute it for simple parametric models.

## Core resources (read/watch one of the following)

## -Paid-

→ Mathematical Statistics and Data Analysis

An undergraduate statistics textbook.

Location:
Section 8.4, "The method of moments," pages 260-267

→ All of Statistics

A very concise introductory statistics textbook.

Location:
Section 9.2, "The method of moments," pages 120-122

## Supplemental resources (the following are optional, but you may find them useful)

## -Paid-

→ Probability and Statistics

An introductory textbook on probability theory and statistics.

Location:
Section 7.6, "Properties of maximum likelihood estimators," subsection "Method of moments," pages 430-432

## See also

- Some other parameter estimation methods include: In exponential families families, the maximum likelihood solution [is equivalent](maximum_likelihood_in_exponential_families) to moment matching.