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Restricted expected multivariate least squares

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  • Fang, Kai-Tai
  • Wang, Song-Gui
  • von Rosen, Dietrich

Abstract

A new approach of estimating parameters in multivariate models is introduced. A fitting function will be used. The idea is to estimate parameters so that the fitting function equals or will be close to its expected value. The function will be decomposed into two parts. From one part, which will be independent of the mean parameters, the dispersion matrix is estimated. This estimator is inserted in the second part which then yields the estimators of the mean parameters. The Growth Curve model, extended Growth Curve model and a multivariate variance components model will illustrate the approach.

Suggested Citation

  • Fang, Kai-Tai & Wang, Song-Gui & von Rosen, Dietrich, 2006. "Restricted expected multivariate least squares," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 619-632, March.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:3:p:619-632
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    References listed on IDEAS

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    1. von Rosen, Dietrich, 1989. "Maximum likelihood estimators in multivariate linear normal models," Journal of Multivariate Analysis, Elsevier, vol. 31(2), pages 187-200, November.
    2. Dietrich Rosen, 1995. "Residuals in the growth curve model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(1), pages 129-136, January.
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