On Identifying Influential Observations in the Presence of Multicollinearity

Abstract

Influential observation is one which either individually or together with several other observations has a demonstrably large impact on the values of various estimates of regression coefficient. It has been suggested by some authors that multicollinearity should be controlled before attempting to measure influence of data point. In using ridge regression to mitigate the effect of multicollinearity, there arises a problem of choosing possible of ridge parameter that guarantees stable regression coefficients in the regression model. This paper seeks to check whether the choice of ridge parameter estimator influences the identified influential data points.

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“…Multicollinearity refers to the high correlation between independent variables in regression analysis [24]. It can lead to instability and decreased explanatory power of the model, making the estimated regression coefficients unreliable or difficult to explain.…”