Principal component analysis for multivariate familial data

Konishi, Sadanori ; Radhakrishna Rao, C. (1992) Principal component analysis for multivariate familial data Biometrika, 79 (3). pp. 631-641. ISSN 0006-3444

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The use of a principal component analysis is considered for multivariate data on families with different numbers of siblings. The coefficients in principal components are given as the eigenvectors of the weighted sums of squares and products matrix from the sibling data. Asymptotic distributions of the eigenvalues and eigenvectors of the estimated covariance matrix are obtained for an elliptical population. Asymptotic distributions of statistics associated with reduction of dimensionality are also derived. The results can be used to construct approximate confidence intervals for the coefficients and variances of principal components.

Item Type:Article
Source:Copyright of this article belongs to Oxford University Press.
Keywords:Asymptotic Distribution; Eigenvalues and Eigenvectors; Elliptical Population; Interval Estimation; Familial Data; Reduction of Dimensionality
ID Code:54761
Deposited On:12 Aug 2011 13:20
Last Modified:12 Aug 2011 13:20

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