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Two-way selection of covariables in multivariate growth curve models

Authors
Journal
Linear Algebra and its Applications
0024-3795
Publisher
Elsevier
Publication Date
Volume
289
Identifiers
DOI: 10.1016/s0024-3795(98)10159-3
Keywords
  • Best Linear Unbiased Estimation
  • Covariance Adjustment
  • Multiple Correlation
Disciplines
  • Computer Science
  • Logic
  • Mathematics

Abstract

Abstract The Growth Curve model introduced by Potthoff and Roy [1] has provided a general format for a variety of growth and repeated measures studies. Statistical inference of this model has often been based on the analysis of covariance model (see e.g. [2], where p measurements are partitioned into the q measurements of the main variables and on p − q covariables. Under the general unstructured model for covariance choosing the full set of p − q covariables results the maximum likelihood estimates (ML) of the model parameters. However, in many practical situations a more efficient estimator can be obtained by choosing fewer covariables. In this paper we propose a computationally efficient method for choosing covariables. This procedure, which is called the two-way selection, is based on the efficiency considerations and on an ordinary variable selection procedure. The method is compared to the method proposed by Fujikoshi and Rao [3].

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