A quasi-score approach to the analysis of ordered catogorical data via a mixed heteroskedastic threshold model

Florence Jaffrezic (1) , Christèle Robert-Granié (1) , J.L. Foulley (1)

1 SGQA - Station de Génétique Quantitative et Appliquée

This article presents an extension of the methodology developed by Gilmour et al. [19], for ordered categorical data, taking into account the heterogeneity of residual variances of latent variables. Heterogeneity of residual variances is described via a structural linear model on log-variances. This method involves two main steps: i) a ?marginalization? with respect to the random effects leading to quasi-score estimators; ii) an approximation of the variance-covariance matrix of the observations which leads to an analogue of the Henderson mixed model equations for continuous Gaussian data. This methodology is illustrated by a numerical example of footshape in sheep.

Mots clés: GENERALIZED LINEAR MIXED MODELS; QUASI-SCORE; HETEROGENEITY OF VARIANCES; THRESHOLD RESPONSE MODEL; MODELES LINEAIRES GENERALISES MIXTES; VARIANCES HETEROGENES; MODELE A SEUILS; OVIN; GENETIQUE DES POPULATIONS; GENETIQUE QUANTITATIVE; MODELE MATHEMATIQUE

Domaines: Génétique des populations [q-bio.PE]

Florence Jaffrezic, Christèle Robert-Granié, J.L. Foulley. A quasi-score approach to the analysis of ordered catogorical data via a mixed heteroskedastic threshold model. Genetics Selection Evolution, 1999, 31, pp.301-318. ⟨hal-00199733⟩