Abstract:
In fitting a linear regression model by the least squares’ method, leverage values play a very important role. They often fo1m the basis of regression diagnostics as measures of influential observations in the explanatory variables. Much work has been done on the detection of high leverage values and a good number of diagnostic measures are now available in the literature. But neither of these methods is effective in the identification of high leverage points when multiple high leverage points are present in the data. In our study we proposed a new method for the identification of multiple high leverage points. The usefulness of this newly proposed method is studied under a variety of leverage structures through Monte Carlo simulation experiments. We also investigated the performance of the newly proposed method as a remedy to multi collinearity problem caused by the presence of multiple high leverage points.
Description:
This Thesis is Submitted to the Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh for The Degree of Master of Philosophy (MPhil)
