Principal Components of Patterned Correlation Matrices

2011 ◽  
pp. 46-48
Keyword(s):  
Principal Components ◽  
Correlation Matrices

Q Analysis of Large Samples

1970 ◽  
Vol 7 (1) ◽  
pp. 104-105 ◽  
Author(s):  
Richard M. Johnson
Keyword(s):  
Factor Analysis ◽  
Principal Components ◽  
Correlation Matrices ◽  
Large Samples ◽  
Unlimited Number ◽  
The Matrix ◽  
One Step ◽  
Number Of Individuals

Q analysis, the factor analysis of person-by-person correlation matrices, becomes unmanageable with large samples. There is, however, a one-step approach for computing principal components of the matrix of correlations among an unlimited number of individuals, so long as the number of response items is limited.

A FAMILY OF MODELS EXPLAINING THE LEVEL-SLOPE-CURVATURE EFFECT

2003 ◽  
Vol 06 (03) ◽  
pp. 239-255 ◽  
Author(s):  
LILIANA FORZANI ◽  
CARLOS TOLMASKY
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Yield Curve ◽  
Correlation Structure ◽  
Spectral Structure ◽  
Curvature Effect ◽  
Correlation Matrices ◽  
The Us ◽  
Components Analysis

One of the most widely used methods to build yield curve models is to use principal components analysis on the correlation matrix of the innovations. R. Litterman and J. Scheinkman found that three factors are enough to explain most of the moves in the case of the US treasury curve. These factors are level, steepness and curvature. Working in the context of commodity futures, G. Cortazar and E. Schwartz found that the spectral structure of the correlation matrices is strikingly similar to those found by R. Litterman and J. Scheinkman. We observe that in both cases the correlation between two different contracts maturing at times t and s is roughly of the form ρ|t-s|, for a certain (fixed) 0 ≤ ρ ≤ 1. Assuming this correlation structure we prove that the observed factors are perturbations of cosine waves and we extend the analysis to multiple curves.

The Foliar Composition of the Oil Palm in West Malaysia

1970 ◽  
Vol 6 (3) ◽  
pp. 191-196 ◽  
Author(s):  
Poon Yew Chin ◽  
J. A. Varley ◽  
J. B. Ward
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Oil Palm ◽  
Principal Component ◽  
Component Analysis ◽  
Foliar Nutrients ◽  
Correlation Matrices ◽  
Partial Correlations

SUMMARYTotal and partial correlations are presented between foliar nutrients in a uniformity trial. The method of principal component analysis is applied to the correlation matrices, comparing values for three fronds (3, g and 17). The results are also compared with values obtained elsewhere, and the use of principal components in relating yield to foliar composition is illustrated.

ON THE SPECTRAL DECOMPOSITION OF EMPIRICAL CORRELATION MATRICES

2001 ◽  
Vol 10 (08) ◽  
pp. 1201-1213 ◽  
Author(s):  
LILIANA FORZANI ◽  
CARLOS F. TOLMASKY
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Spectral Decomposition ◽  
Correlation Matrix ◽  
Yield Curve ◽  
Empirical Correlation ◽  
Spectral Structure ◽  
Correlation Matrices ◽  
The Us ◽  
Components Analysis

One of the most widely used methods to build yield curve models is to use principal components analysis on the correlation matrix of the innovations. R. Litterman and J. Scheinkman found that three factors are enough to explain most of the moves in the case of the US treasury curve. These factors are level, steepness and curvature. Working in the context of commodity futures, G. Cortazar and E. Schwartz found that the spectral structure of the correlation matrices is strikingly similar to those found by R. Litterman and J. Scheinkman. We observe that in both cases the correlation between two different contracts maturing at times t and s is roughly of the form ρ|t-s|, for a certain (fixed) 0≤ρ≤1. Assuming this correlation structure we prove that the observed factors are perturbations of cosine waves.

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