Principal Component Analysis

S. P. Mukherjee (), Bikas K. Sinha and Asis Kumar Chattopadhyay ()
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S. P. Mukherjee: University of Calcutta, Department of Statistics
Bikas K. Sinha: Indian Statistical Institute
Asis Kumar Chattopadhyay: University of Calcutta, Department of Statistics

Chapter Chapter 9 in Statistical Methods in Social Science Research, 2018, pp 95-102 from Springer

Abstract: Abstract Principal component analysis (PCA) is a method for dimension reduction tool in order to reduce a large set of variables to a small set of components that still contains most of the information in the original data set. Under PCS, we transform a number of correlated variables into a smaller set of uncorrelated components called principal components.

Keywords: Principal component; Correlation matrix; Dispersion matrix; Eigenvalue; Eigenvector; Screen plot; Biplot; Dimension reduction; Independent components (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-2146-7_9

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DOI: 10.1007/978-981-13-2146-7_9

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