MAXIMUM-LIKELIHOOD VARIANCE-COMPONENTS ESTIMATION FOR BINARY DATA

We consider a class of probit normal models for binary data and descri be ML and REML estimation of variance components for that class as wel l as best prediction for the realized values of the random effects. ML estimates are calculated using an EM algorithm; for complicated model s EM includes a Gibbs step. The computations are illustrated through t wo examples.