SIAM Journal on Scientific and Statistical Computing
Statistical analysis with missing data
Statistical analysis with missing data
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Common principal components & related multivariate models
Common principal components & related multivariate models
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Computational Statistics & Data Analysis
Common component analysis for multiple covariance matrices
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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A method for simultaneous modelling of the Cholesky decomposition of several covariance matrices is presented. We highlight the conceptual and computational advantages of the unconstrained parameterization of the Cholesky decomposition and compare the results with those obtained using the classical spectral (eigenvalue) and variance-correlation decompositions. All these methods amount to decomposing complicated covariance matrices into ''dependence'' and ''variance'' components, and then modelling them virtually separately using regression techniques. The entries of the ''dependence'' component of the Cholesky decomposition have the unique advantage of being unconstrained so that further reduction of the dimension of its parameter space is fairly simple. Normal theory maximum likelihood estimates for complete and incomplete data are presented using iterative methods such as the EM (Expectation-Maximization) algorithm and their improvements. These procedures are illustrated using a dataset from a growth hormone longitudinal clinical trial.