# List the advantages and disadvantages of maximum likelihood methods

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**Advantages. **

**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.

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**Disadvantages. **

**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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