method of moments
(1.4 hours to learn)
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.
This concept has the prerequisites:
- expectation and variance (The method of moments requires estimating moments.)
- 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)
→ 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)
→ 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
- Some other parameter estimation methods include:exponential families families, the maximum likelihood solution [is equivalent](maximum_likelihood_in_exponential_families) to moment matching.
- create concept: shift + click on graph
- change concept title: shift + click on existing concept
- link together concepts: shift + click drag from one concept to another
- remove concept from graph: click on concept then press delete/backspace
- add associated content to concept: click the small circle that appears on the node when hovering over it
- other actions: use the icons in the upper right corner to optimize the graph placement, preview the graph, or download a json representation