Abstract

Bilinear models with three types of effects are considered: fixed effects, random effects and latent variable effects. In the literature, bilinear models with random effects and bilinear models with latent variables have been discussed but there are no results available when combining random effects and latent variables. It is shown, via appropriate vector space decompositions, how to remove the random effects so that a well-known model comprising only fixed effects and latent variables is obtained. The spaces are chosen so that the likelihood function can be factored in a convenient and interpretable way. To obtain explicit estimators, an important standardization constraint on the random effects is assumed to hold. A theorem is presented where a complete solution to the estimation problem is given.

Keywords

Fixed effects; Growth curve model; Likelihood-based estimates; Random effects; Rank restrictions; 62F10; 62F30

Published in

Japanese Journal of Statistics and Data Science
2020, Volume: 3, number: 1, pages: 63-72
Publisher: SPRINGERNATURE

SLU Authors

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    
  • Linköping University

UKÄ Subject classification

Probability Theory and Statistics


Publication identifier

DOI: https://doi.org/10.1007/s42081-019-00050-2

Permanent link to this page (URI)

https://res.slu.se/id/publ/123157