Maximum Likelihood Variance Components Estimation for Binary Data

Abstract We consider a class of probit-normal models for binary data and describe ML and REML estimation of variance components for that class as well as best prediction for the realized values of the random effects. ML estimates are calculated using an EM algorithm; for complicated models EM includes a Gibbs step. The computations are illustrated through two examples.

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