Difference between revisions of "Point estimator"
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A [[Statistical estimator|statistical estimator]] whose values are points in the set of values of the quantity to be estimated. | A [[Statistical estimator|statistical estimator]] whose values are points in the set of values of the quantity to be estimated. | ||
| − | Suppose that in the realization | + | Suppose that in the realization $ x = ( x _ {1} \dots x _ {n} ) ^ {T} $ |
| + | of the random vector $ X = ( X _ {1} \dots X _ {n} ) ^ {T} $, | ||
| + | taking values in a sample space $ ( \mathfrak X , {\mathcal B} , {\mathsf P} _ \theta ) $, | ||
| + | $ \theta = ( \theta _ {1} \dots \theta _ {k} ) ^ {T} \in \Theta \subset \mathbf R ^ {k} $, | ||
| + | the unknown parameter $ \theta $( | ||
| + | or some function $ g ( \theta ) $) | ||
| + | is to be estimated. Then any statistic $ T _ {n} = T _ {n} ( X) $ | ||
| + | producing a mapping of the set $ \mathfrak X $ | ||
| + | into $ \Theta $( | ||
| + | or into the set of values of $ g ( \theta ) $) | ||
| + | is called a point estimator of $ \theta $( | ||
| + | or of the function $ g ( \theta ) $ | ||
| + | to be estimated). Important characteristics of a point estimator $ T _ {n} $ | ||
| + | are its mathematical expectation | ||
| − | + | $$ | |
| + | {\mathsf E} _ \theta \{ T _ {n} \} = \ | ||
| + | \int\limits _ {\mathfrak X } T _ {n} ( x) d {\mathsf P} _ \theta ( x) | ||
| + | $$ | ||
and the covariance matrix | and the covariance matrix | ||
| − | + | $$ | |
| + | {\mathsf E} _ \theta \{ ( T _ {n} - {\mathsf E} \{ T _ {n} \} ) | ||
| + | ( T _ {n} - {\mathsf E} \{ T _ {n} \} ) ^ {T} \} . | ||
| + | $$ | ||
| − | The vector | + | The vector $ d ( X) = T _ {n} ( X) - g ( \theta ) $ |
| + | is called the error vector of the point estimator $ T _ {n} $. | ||
| + | If | ||
| − | + | $$ | |
| + | b ( \theta ) = \ | ||
| + | {\mathsf E} _ \theta \{ d ( X) \} = \ | ||
| + | {\mathsf E} _ \theta \{ T _ {n} \} - g ( \theta ) | ||
| + | $$ | ||
| − | is the zero vector for all | + | is the zero vector for all $ \theta \in \Theta $, |
| + | then one says that $ T _ {n} $ | ||
| + | is an unbiased estimator of $ g ( \theta ) $ | ||
| + | or that $ T _ {n} $ | ||
| + | is free of systematic errors; otherwise, $ T _ {n} $ | ||
| + | is said to be biased, and the vector $ b ( \theta ) $ | ||
| + | is called the bias or systematic error of the point estimator. The quality of a point estimator can be defined by means of the risk function (cf. [[Risk of a statistical procedure|Risk of a statistical procedure]]). | ||
====References==== | ====References==== | ||
<table><TR><TD valign="top">[1]</TD> <TD valign="top"> H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top"> I.A. Ibragimov, R.Z. [R.Z. Khas'minskii] Has'minskii, "Statistical estimation: asymptotic theory" , Springer (1981) (Translated from Russian)</TD></TR></table> | <table><TR><TD valign="top">[1]</TD> <TD valign="top"> H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top"> I.A. Ibragimov, R.Z. [R.Z. Khas'minskii] Has'minskii, "Statistical estimation: asymptotic theory" , Springer (1981) (Translated from Russian)</TD></TR></table> | ||
| − | |||
| − | |||
====Comments==== | ====Comments==== | ||
| − | |||
====References==== | ====References==== | ||
<table><TR><TD valign="top">[a1]</TD> <TD valign="top"> E.L. Lehmann, "Theory of point estimation" , Wiley (1983)</TD></TR></table> | <table><TR><TD valign="top">[a1]</TD> <TD valign="top"> E.L. Lehmann, "Theory of point estimation" , Wiley (1983)</TD></TR></table> | ||
Latest revision as of 08:06, 6 June 2020
A statistical estimator whose values are points in the set of values of the quantity to be estimated.
Suppose that in the realization $ x = ( x _ {1} \dots x _ {n} ) ^ {T} $ of the random vector $ X = ( X _ {1} \dots X _ {n} ) ^ {T} $, taking values in a sample space $ ( \mathfrak X , {\mathcal B} , {\mathsf P} _ \theta ) $, $ \theta = ( \theta _ {1} \dots \theta _ {k} ) ^ {T} \in \Theta \subset \mathbf R ^ {k} $, the unknown parameter $ \theta $( or some function $ g ( \theta ) $) is to be estimated. Then any statistic $ T _ {n} = T _ {n} ( X) $ producing a mapping of the set $ \mathfrak X $ into $ \Theta $( or into the set of values of $ g ( \theta ) $) is called a point estimator of $ \theta $( or of the function $ g ( \theta ) $ to be estimated). Important characteristics of a point estimator $ T _ {n} $ are its mathematical expectation
$$ {\mathsf E} _ \theta \{ T _ {n} \} = \ \int\limits _ {\mathfrak X } T _ {n} ( x) d {\mathsf P} _ \theta ( x) $$
and the covariance matrix
$$ {\mathsf E} _ \theta \{ ( T _ {n} - {\mathsf E} \{ T _ {n} \} ) ( T _ {n} - {\mathsf E} \{ T _ {n} \} ) ^ {T} \} . $$
The vector $ d ( X) = T _ {n} ( X) - g ( \theta ) $ is called the error vector of the point estimator $ T _ {n} $. If
$$ b ( \theta ) = \ {\mathsf E} _ \theta \{ d ( X) \} = \ {\mathsf E} _ \theta \{ T _ {n} \} - g ( \theta ) $$
is the zero vector for all $ \theta \in \Theta $, then one says that $ T _ {n} $ is an unbiased estimator of $ g ( \theta ) $ or that $ T _ {n} $ is free of systematic errors; otherwise, $ T _ {n} $ is said to be biased, and the vector $ b ( \theta ) $ is called the bias or systematic error of the point estimator. The quality of a point estimator can be defined by means of the risk function (cf. Risk of a statistical procedure).
References
| [1] | H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946) |
| [2] | I.A. Ibragimov, R.Z. [R.Z. Khas'minskii] Has'minskii, "Statistical estimation: asymptotic theory" , Springer (1981) (Translated from Russian) |
Comments
References
| [a1] | E.L. Lehmann, "Theory of point estimation" , Wiley (1983) |
Point estimator. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Point_estimator&oldid=16171