In statistics, Cook's distance or Cook's D is a commonly used estimate of the influence of a data point when performing a least-squares regression analysis. In a practical ordinary least squares analysis, Cook's distance can be used in several ways: to indicate influential data points that are particularly worth checking for validity; or to indicate regions of the design space where it would be good to be able to obtain more data points. It is named after the American statistician R. Dennis Cook, who introduced the concept in 1977.
Data points with large residuals (outliers) and/or high leverage may distort the outcome and accuracy of a regression. Cook's distance measures the effect of deleting a given observation. Points with a large Cook's distance are considered to merit closer examination in the analysis.
For the algebraic expression, first define
where is the error term, is the coefficient matrix, is the number of covariates or predictors for each observation, and is the design matrix including a constant. The least squares estimator then is , and consequently the fitted (predicted) values for the mean of are
where is the projection matrix (or hat matrix). The -th diagonal element of , given by , is known as the leverage of the -th observation. Similarly, the -th element of the residual vector is denoted by .
Cook's distance of observation is defined as the sum of all the changes in the regression model when observation is removed from it
where p is the rank of the model and is the fitted response value obtained when excluding , and is the mean squared error of the regression model.
Equivalently, it can be expressed using the leverage ():
There are different opinions regarding what cut-off values to use for spotting highly influential points. Since Cook's distance is in the metric of an F distribution with and (as defined for the design matrix above) degrees of freedom, the median point (i.e., ) can be used as a cut-off. Since this value is close to 1 for large , a simple operational guideline of has been suggested.
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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 the independent variables. That is, high-leverage points have no neighboring points in space, where is the number of independent variables in a regression model. This makes the fitted model likely to pass close to a high leverage observation.
In statistics, the projection matrix , sometimes also called the influence matrix or hat matrix , maps the vector of response values (dependent variable values) to the vector of fitted values (or predicted values). It describes the influence each response value has on each fitted value. The diagonal elements of the projection matrix are the leverages, which describe the influence each response value has on the fitted value for that same observation.
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 observation 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 .
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