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Outlier Detection in Ridge Regression Model Under Stochastic Linear Restrictions
Abdolrahman Rasekh , Behzad Mansouri , Narges Hedayatpoor
Abstract:   (447 Views)

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.

Keywords: Collinearity, Ridg Regression, Ridg Regression under Linear Restriction, Outlier, Mean Shift Method
Full-Text [PDF 5957 kb]   (70 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2015/02/7 | Accepted: 2018/05/18
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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