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Principal Component Analysis and its Application to Global Inflation Rates and Per Capita GDP of Different Countries

Kumar, Nishant (2006) Principal Component Analysis and its Application to Global Inflation Rates and Per Capita GDP of Different Countries. Masters thesis, Indian Institute of Science Education and Research Kolkata.

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    Abstract

    Principal component Analysis (PCA) is a method of reducing the dimension of the data. In practice, we may have a number of variables which are responsible for the effects, but all may not be of much importance. In PCA, we consider a couple of linear combinations of the variables so that these linear combinations will be sufficient to explain most of the variabilities present in the data. The aim of the transformation is that the first component has as high a variance as possible and the subsequent components have as high a variance with an added constraint that it should be uncorrelated to all the previous principal components. This technique has been applied to the aforementioned data sets and some very interesting results are obtained.

    Item Type: Thesis (Masters)
    Additional Information: Supervisor: Prof. Asok K. Nanda
    Uncontrolled Keywords: Principal Component Analysis; PCA; Global Inflation Rate; Per Capita GDP; GDP
    Subjects: Q Science > QA Mathematics
    Divisions: Faculty of Engineering, Science and Mathematics > School of Mathematics
    Depositing User: IISER Kolkata Librarian
    Date Deposited: 25 Nov 2013 16:47
    Last Modified: 06 Jan 2015 10:50
    URI: http://eprints.iiserkol.ac.in/id/eprint/98

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