On Identifying Influential Observations in the Presence of Multicollinearity  

作　　者：Chinwendu Alice Uzuke Ifeyinwa Christiana Ezeilo Chinwendu Alice Uzuke;Ifeyinwa Christiana Ezeilo(Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria)

机构地区：[1]Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria

出　　处：《Open Journal of Statistics》2021年第2期290-302,共13页统计学期刊（英文）

摘　　要：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</span></span><span style="font-family:Verdana;">.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</span></span><span style="font-family:Verdana;">.

关 键 词：Multicollinerity Ridge Parameter Influential Measures OUTLIERS Leverage Point 

分 类 号：O17[理学—数学]