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Robust Methods for Eliminating Multicollinearity in Linear Regression Models

Student: Daria Krasnova

Supervisor: Elena R. Goryainova

Faculty: Faculty of Economic Sciences

Educational Programme: Statistical Modelling and Actuarial Science (Master)

Year of Graduation: 2019

The work presents a research devoted to solutions of the problem of data multicollinearity. The reduction of dimensions and application of principal component analysis was applied to the linear regression problems. The author describes robust methods that were used in the correlation matrix construction for principal components. In order to estimate the quality of the proposed methods, there have been done some numerical experiments with normal and heavy-tailed data. It is shown that principal component robust approaches have a stronger generalizing ability especially when there are outliers or clusters of outliers.

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