Estimator

Estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished.

There are point and interval estimators. The point estimators yield single-valued results, although this includes the possibility of single vector-valued results and results that can be expressed as a single function. This is in contrast to an interval estimator, where the result would be a range of plausible values (or vectors or functions).

Types of Estimators

Estimators are applied in the fields of statistics and signal processing. There are several types of estimators, including:

• Unbiased Estimators
• Minimum Variance Unbiased Estimators
• Maximum Likelihood Estimators
• Bayes Estimators
• Method of Moments Estimators

Properties of Estimators

Estimators have certain properties that can be used to compare and evaluate them. These properties include:

• Bias
• Variance
• Efficiency
• Consistency

Applications of Estimators

Estimators are used in various fields, including:

• Econometrics
• Machine learning
• Signal processing
• Data analysis

See Also

• Estimation theory
• Estimation of covariance matrices
• Estimation of signal parameters
• Estimation in statistics

References

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  Estimator

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  Estimator

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  Good estimator

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  Bad estimator

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  Comparison between a good and a bad estimator