Selberg integral: Difference between revisions

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In [[statistics]], high-leverage points are those that are [[outlier]]s with respect to the [[independent variables]]. [[leverage (statistics)|Leverage point]]s are those that cause large changes in the parameter estimates when they are deleted. Although an influential point will typically have high leverage, a high leverage point is not necessarily an influential point. The [[leverage (statistics)|leverage]] is typically defined as the diagonal of the [[hat matrix]]


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:<math> H = X(X'X)^{-1}X'.  \, </math>
 
'''Partial leverage''' is used to measure the contribution of the individual independent variables to the leverage of each observation. That is, if ''h''<sub>''i''</sub> is the ''i''<sup>th</sup> row of the diagonal of the hat matrix, the partial leverage is a measure of how ''h''<sub>''i''</sub> changes as a variable is added to the regression model.
 
The partial leverage is computed as:
 
:<math>
\left(\mathrm{PL}_j\right)_i = \frac{\left(X_{j\bullet[j]}\right)_i^2}{\sum_{k=1}^n\left(X_{j\bullet[j]}\right)_k^2}
</math>
 
where
:''j'' = index of independent variable
:''i'' = index of observation
:''X''<sub>''j''&middot;[''j'']</sub> = [[errors and residuals in statistics|residuals]] from regressing ''X''<sub>''j''</sub> against the remaining independent variables
 
Note that the partial leverage is the leverage of the ''i''<sup>th</sup> point in the [[partial regression plot]] for the ''j''<sup>th</sup> variable. Data points with large partial leverage for an independent variable can exert undue influence on the selection of that variable in automatic regression model building procedures.
 
==See also==
* [[Partial residual plot]]
* [[Partial regression plot]]
* [[Variance inflation factor]] for a multi-linear fit
* [[Scatterplot matrix]]
 
==External links==
* [http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/partleve.htm Partial Leverage Plot]
 
==References==
* {{cite book
|title = Modern Regression Methods
|author = Tom Ryan
|publisher = John Wiley
|year = 1997}}
* {{cite book
|title = Applied Linear Statistical Models
|edition = 3rd
|author = Neter, Wasserman, and Kunter
|year = 1990
|publisher = Irwin}}
* {{cite book
|title = Applied Regression Analysis
|edition = 3rd
|author = Draper and Smith
|publisher = John Wiley
|year = 1998}}
* {{cite book
|title = Residuals and Influence in Regression
|author = Cook and Weisberg
|publisher = Chapman and Hall
|year = 1982}}
* {{cite book
|title = Regression Diagnostics
|author = Belsley, Kuh, and Welsch
|publisher = John Wiley
|year = 1980}}
* {{cite journal
|title = Efficient Computing of Regression Diagnostiocs
|author = Paul Velleman
|coauthor = Roy Welsch
|journal = The American Statistician
|month = November
|year = 1981
|volume = 35
|pages = 234–242
|doi = 10.2307/2683296
|issue = 4
|publisher = American Statistical Association
|jstor = 2683296}}
 
{{NIST-PD}}
 
[[Category:Regression diagnostics]]

Latest revision as of 21:02, 7 December 2013

In statistics, high-leverage points are those that are outliers with respect to the independent variables. Leverage points are those that cause large changes in the parameter estimates when they are deleted. Although an influential point will typically have high leverage, a high leverage point is not necessarily an influential point. The leverage is typically defined as the diagonal of the hat matrix

Partial leverage is used to measure the contribution of the individual independent variables to the leverage of each observation. That is, if hi is the ith row of the diagonal of the hat matrix, the partial leverage is a measure of how hi changes as a variable is added to the regression model.

The partial leverage is computed as:

where

j = index of independent variable
i = index of observation
Xj·[j] = residuals from regressing Xj against the remaining independent variables

Note that the partial leverage is the leverage of the ith point in the partial regression plot for the jth variable. Data points with large partial leverage for an independent variable can exert undue influence on the selection of that variable in automatic regression model building procedures.

See also

External links

References

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