Flexible random intercept models for binary outcomes using mixtures of normals

Abstract

Random intercept models for binary data are useful tools for addressing between-subject heterogeneity. Unlike linear models, the non-linearity of link functions used for binary data force a distinction between marginal and conditional interpretations. This distinction is blurred in probit models with a normally distributed random intercept because the… (More)
DOI: 10.1016/j.csda.2006.09.031

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