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.