RT - Journal Article T1 - Outlier Detection in Ridge Regression Model Under Stochastic Linear Restrictions JF - JSS YR - 2019 JO - JSS VO - 13 IS - 1 UR - http://jss.irstat.ir/article-1-358-en.html SP - 117 EP - 137 K1 - Collinearity K1 - Ridg Regression K1 - Ridg Regression under Linear Restriction K1 - Outlier K1 - Mean Shift Method AB - The study of regression diagnostic, including identification of the influential observations and outliers, is of particular importance. The sensitivity of least squares estimators to the outliers and influential observations lead to extending the regression diagnostic in order to provide criteria to assess the anomalous observations. Detecting influential observations and outliers in the presence of collinearity is a complicated task, in the sense that collinearity may cover some of the unusual data. One of the considerable methods to identify outliers is the mean shift outliers method. In this article, we extend the mean shift outliers method to the ridge estimates under linear stochastic restrictions, which is used to reduce the effect of collinearity, and to provide the test statistic to identify the outliers in these estimators. Finally, we show the ability of our proposed method using a practical example of real data. LA eng UL http://jss.irstat.ir/article-1-358-en.html M3 10.29252/jss.13.1.117 ER -