Advantages and disadvantages of maximum likelihood methods

List the advantages and disadvantages of maximum likelihood methods

Advantages.

• Simple to apply

• Lower variance than other methods (i.e. estimation method least affected by sampling error) and unbiased as the sample size increases.

• The method is statistically well understood

• Able to analyze statistical models with different characters on the same basis. Maximum likelihood provides a consistent approach to parameter estimation  problems. This means that maximum likelihood estimates can be developed for a large variety of estimation situations.

• Once a maximum-likelihood estimator is derived, the general theory of maximum-likelihood estimation provides standard errors, statistical tests, and other results useful for statistical inference.

Disadvantages.

• Computationally intensive and so extremely slow (though this is becoming much less of an issue)

• Frequently requires strong assumptions about the structure of the data

• The estimates that are obtained using this method are often biased. That is, they contain a systematic error of estimation. This is true for small samples. The optimality properties may not apply for small samples.

• MLE is inapplicable for the analysis of non-regular populations (Non-regular distributions are models where a parameter value is constrained by a single observed value)

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Related questions:

• What are the advantages of small decision trees in comparison to larger ones

• What are the properties of maximum-likelihood estimators

advantages of maximum likelihood methods

disadvantage of maximum likelihood methods

maximum likelihood methods have lower variance especially in increased sample size