Point-Estimation Function

A Point-Estimation Function is a real-valued function that can provide a point estimate.

• Context:
  • It can represent an Estimation Function.
  • It can range from being an Abstract Point Estimation Function to being a Point Estimation Structure.
  • It can range from being a Heuristic Point Estimation Function to being a Learned Point Estimation Function.
• Example(s):
  • a Regressed Linear Function.
  • …
• Counter-Example(s):
  • a Class Prediction Function.
  • a Rank Prediction Function.
• See: Point Estimation Task, Range Estimate, eEPC Function.

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References

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/estimator#Background Retrieved:2015-6-28.
  • An "estimator" or “point estimate” is a statistic (that is, a function of the data) that is used to infer the value of an unknown parameter in a statistical model. The parameter being estimated is sometimes called the estimand. It can be either finite-dimensional (in parametric and semi-parametric models), or infinite-dimensional (semi-parametric and non-parametric models). If the parameter is denoted θ then the estimator is traditionally written by adding a circumflex over the symbol: [math]\displaystyle{ \scriptstyle\hat\theta }[/math]. Being a function of the data, the estimator is itself a random variable; a particular realization of this random variable is called the "estimate". Sometimes the words "estimator" and "estimate" are used interchangeably.

    The definition places virtually no restrictions on which functions of the data can be called the "estimators". The attractiveness of different estimators can be judged by looking at their properties, such as unbiasedness, mean square error, consistency, asymptotic distribution, etc.. The construction and comparison of estimators are the subjects of the estimation theory. In the context of decision theory, an estimator is a type of decision rule, and its performance may be evaluated through the use of loss functions.

    When the word "estimator" is used without a qualifier, it usually refers to point estimation. The estimate in this case is a single point in the parameter space. Other types of estimators also exist: interval estimators, where the estimates are subsets of the parameter space.

    The problem of density estimation arises in two applications. Firstly, in estimating the probability density functions of random variables and secondly in estimating the spectral density function of a time series. In these problems the estimates are functions that can be thought of as point estimates in an infinite dimensional space, and there are corresponding interval estimation problems.

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