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*List the properties of Maximum-Likelihood Estimators*

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__Properties of Maximum-Likelihood Estimators__

Maximum-likelihood estimators
have the following general properties:

- Maximum-likelihood estimators are consistent; as the sample size increases, the estimator converges in probability to its true value

- They are asymptotically unbiased, although they may be biased in small samples.

- They are asymptotically efficient—no asymptotically unbiased estimator has a smaller asymptotic variance.

- They are asymptotically normally distributed. As the sample size increases, the distribution of the estimator is normal. No other estimator will have a smaller standard error.

- If there is a sufficient statistic for a parameter, then the maximum-likelihood estimator of the parameter is a function of a sufficient statistic.
- A sufficient statistic is a statistic that exhausts all of the information in the sample about the parameter of interest.

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