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Principal Component Analysis of Large Dispersion Matrices
C. R. Narayanaswamy and D. Raghavarao
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 40, No. 2 (1991), pp. 309-316
Stable URL: http://www.jstor.org/stable/2347595
Page Count: 8
You can always find the topics here!Topics: Eigenvectors, Eigenvalues, Principal components analysis, Matrices, Securities returns, Coincidence, Cosine function, Statistical discrepancies, Computer technology, Mathematical problems
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Sometimes it may be necessary to find the first few dominant principal components of a dispersion (covariance) matrix of large order. For many computers such problems could be too big to handle. This paper provides an effective approach to such situations through a series of splitting and merging operations on subsets of variables. An illustration is provided with applications of the suggested technique to stock price data.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1991 Royal Statistical Society