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has been cited by the following article:

• TITLE: Detecting Global Influential Observations in Liu Regression Model

  AUTHORS: Aboobacker Jahufer

  KEYWORDS: Liu Estimator; Global Influential Observations; Diagnostics; Multicollinearity; Case Deletion; Approximate Deletion Formulas

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.1, February 20, 2013

  ABSTRACT: In linear regression analysis, detecting anomalous observations is an important step for model building process. Various influential measures based on different motivational arguments and designed to measure the influence of observations on different aspects of various regression results are elucidated and critiqued. The presence of influential observations in the data is complicated by the presence of multicollinearity. In this paper, when Liu estimator is used to mitigate the effects of multicollinearity the influence of some observations can be drastically modified. Approximate deletion formulas for the detection of influential points are proposed for Liu estimator. Two real macroeconomic data sets are used to illustrate the methodologies proposed in this paper.