Editing Maximum likelihood estimation (section)

== See also ==
{{Portal|Mathematics}}

=== Related concepts===
* [[Akaike information criterion]]: a criterion to compare statistical models, based on MLE 
* [[Extremum estimator]]: a more general class of estimators to which MLE belongs
* [[Fisher information]]: information matrix, its relationship to covariance matrix of ML estimates
* [[Mean squared error]]: a measure of how 'good' an estimator of a distributional parameter is (be it the maximum likelihood estimator or some other estimator)
* [[RANSAC]]: a method to estimate parameters of a mathematical model given data that contains [[outliers]]
* [[Rao–Blackwell theorem]]: yields a process for finding the best possible unbiased estimator (in the sense of having minimal [[mean squared error]]); the MLE is often a good starting place for the process
* [[Likelihood-ratio test#Asymptotic distribution: Wilks' theorem|Wilks' theorem]]: provides a means of estimating the size and shape of the region of roughly equally-probable estimates for the population's parameter values, using the information from a single sample, using a [[chi-squared distribution]]

=== Other estimation methods ===
* [[Generalized method of moments]]: methods related to the likelihood equation in maximum likelihood estimation
* [[M-estimator]]: an approach used in robust statistics
* [[Maximum a posteriori]] (MAP) estimator: for a contrast in the way to calculate estimators when prior knowledge is postulated
* [[Maximum spacing estimation]]: a related method that is more robust in many situations
* [[Principle of maximum entropy|Maximum entropy estimation]]
* [[Method of moments (statistics)]]: another popular method for finding parameters of distributions
* [[Method of support]], a variation of the maximum likelihood technique
* [[Minimum-distance estimation]]
* [[Partial likelihood methods for panel data]]
* [[Quasi-maximum likelihood]] estimator: an MLE estimator that is misspecified, but still consistent
* [[Restricted maximum likelihood]]: a variation using a likelihood function calculated from a transformed set of data