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In statistics, an influential observation is an observation for a statistical calculation whose deletion from the dataset would noticeably change the result of the calculation. In particular, in
regression analysis In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one ...
an influential observation is one whose deletion has a large effect on the parameter estimates.


Assessment

Various methods have been proposed for measuring influence. Assume an estimated regression \mathbf = \mathbf \mathbf + \mathbf, where \mathbf is an ''n''×1 column vector for the response variable, \mathbf is the ''n''×''k''
design matrix In statistics and in particular in regression analysis, a design matrix, also known as model matrix or regressor matrix and often denoted by X, is a matrix of values of explanatory variables of a set of objects. Each row represents an individual ...
of explanatory variables (including a constant), \mathbf is the ''n''×1 residual vector, and \mathbf is a ''k''×1 vector of estimates of some population parameter \mathbf \in \mathbb^. Also define \mathbf \equiv \mathbf \left(\mathbf^ \mathbf \right)^ \mathbf^, the projection matrix of \mathbf. Then we have the following measures of influence: # \text_ \equiv \mathbf - \mathbf_ = \frac, where \mathbf_ denotes the coefficients estimated with the ''i''-th row \mathbf_ of \mathbf deleted, h_ = \mathbf_ \left( \mathbf^ \mathbf \right)^ \mathbf_^ denotes the ''i''-th value of matrix's \mathbf main diagonal. Thus DFBETA measures the difference in each parameter estimate with and without the influential point. There is a DFBETA for each variable and each observation (if there are ''N'' observations and ''k'' variables there are N·k DFBETAs). Table shows DFBETAs for the third dataset from Anscombe's quartet (bottom left chart in the figure):


Outliers, leverage and influence

An
outlier In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to a variability in the measurement, an indication of novel data, or it may be the result of experimental error; the latter are ...
may be defined as a data point that differs significantly from other observations. A
high-leverage point In statistics and in particular in regression analysis, leverage is a measure of how far away the independent variable values of an observation are from those of the other observations. ''High-leverage points'', if any, are outliers with respect to ...
are observations made at extreme values of independent variables. Both types of atypical observations will force the regression line to be close to the point. In Anscombe's quartet, the bottom right image has a point with high leverage and the bottom left image has an outlying point.


See also

* Influence function (statistics) *
Outlier In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to a variability in the measurement, an indication of novel data, or it may be the result of experimental error; the latter are ...
* Leverage ** Partial leverage *
Regression analysis In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one ...
* * Anomaly detection


References


Further reading

* * {{cite book , first=Peter , last=Kennedy , author-link=Peter Kennedy (economist) , chapter=Robust Estimation , title=A Guide to Econometrics , location=Cambridge , publisher=The MIT Press , edition=Fifth , year=2003 , isbn=0-262-61183-X , pages=372–388 , chapter-url=https://books.google.com/books?id=B8I5SP69e4kC&pg=PA372 Actuarial science Regression diagnostics Robust statistics