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 an influential point is one whose deletion has a large effect on the parameter estimates.
Various methods have been proposed for measuring influence. Assume an estimated regression , where is an n×1 column vector for the response variable, is the n×k design matrix of explanatory variables (including a constant), is the n×1 residual vector, and is a k×1 vector of estimates of some population parameter . Also define , the projection matrix of . Then we have the following measures of influence:
- , where denotes the coefficients estimated with the i-th row of deleted, denotes the i-th row of . Thus DFBETA measures the difference in each parameter estimate with and without the influential point. There is a DFBETA for each point and each observation (if there are N points and k variables there are N·k DFBETAs).
- Cook's D measures the effect of removing a data point on all the parameters combined.
Outliers, leverage and influence
An outlier may be defined as a surprising data point. Leverage is a measure of how much the estimated value of the dependent variable changes when the point is removed. There is one value of leverage for each data point. Data points with high leverage force the regression line to be close to the point. In Anscombe's quartet, only the bottom right image has a point with high leverage.
- Regression analysis
- Cook's distance#Detecting highly influential observations
- Anomaly detection
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- "Outliers and DFBETA" (PDF). Archived (PDF) from the original on May 11, 2013.
- Hurvich, Clifford. "Simple Linear Regression VI: Leverage and Influence" (PDF). NYU Stern. Archived (PDF) from the original on September 21, 2006.
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