Sammanfattning

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.

Nyckelord

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

Publicerad i

Japanese Journal of Statistics and Data Science
2020, Volym: 3, nummer: 1, sidor: 63-72
Utgivare: SPRINGERNATURE

SLU författare

• von Rosen, Dietrich

  • Institutionen för energi och teknik, Sveriges lantbruksuniversitet
    
  • Linköpings universitet

UKÄ forskningsämne

Sannolikhetsteori och statistik


Publikationens identifierare

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

Permanent länk till denna sida (URI)

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